Two years ago, in January of 2021, I moved to Washington, DC. It was a difficult week to come to this nation’s capital, but I am grateful I did. I spent the next year serving as a technology advisor to Senator Ron Wyden (D-OR) and learning about being a staffer in Congress through the TechCongress Congressional Innovation Fellowship. I had incredible mentors and champions both on and off the Hill, and I was able to work on many different important tech policy issues.
To apply to TechCongress, visit techcongress.io/apply. You can read about what motivated me to work in Congress and see my answers to the 2021 cohort application as a reference.
One of the things that I am most proud of having had the opportunity to work on is the Algorithmic Accountability Act of 2022, introduced in February of that year. The bill reflects the thinking and input from many, many brilliant people, but I’m glad to have been one of the key staffers crafting this text behind the scenes. The Algorithmic Accountability Act of 2022 has some really cool ideas articulated in it, but (as I experienced when I first started on the Hill back in January of 2021) legislation can be difficult to read for those who aren’t deeply familiar with it.
I wanted to write something to explain some of the things that I think make this bill really interesting and exciting, and stuff I think makes it an important piece of legislation not just for AI governance but for how we think about technology policy more broadly. I also wanted to explain some of the thinking behind the bill in case it might inspire others or challenge people to re-examine how they approach policymakers to make change.
What follows is a cleaned up, edited, and expanded version of what was originally shared on Twitter (and later Mastodon).
Due to length, I’ve split it up into parts:
Algorithmic Accountability from 10,000ft (that’s this!)
Why I’m (still) hyped about the Algorithmic Accountability Act of 2022
How to get into AI policy (coming soon)
Let’s get into it!
For starters: the Algorithmic Accountability Act of 2022 is a bill “to direct the Federal Trade Commission to require impact assessments of automated decision systems and augmented critical decision processes, and for other purposes.”
But what does that mean?
Let’s back up a bit. The Algorithmic Accountability Act of 2022 is a piece of legislation that was introduced in the 117th Congress of the United States. It is a bill, which is a document that has a bunch of legal-sounding language that was written and submitted for Congress to consider as something to turn into a law. The 2022 Algorithmic Accountability Act is actually a revision (an update) and reintroduction (a re-submission for consideration) of an earlier bill originally introduced in 2019, which, itself, was an independent introduction of some text that was originally included as a piece of a different 2019 bill called the Mind Your Own Business Act, which was also revised and reintroduced earlier in 2021).
You might be noticing a pattern. It’s pretty common for US federal bills to be revised, reintroduced, remixed, and otherwise Frankensteined into different versions as people make edits, incorporate feedback, and even change office. The Algorithmic Accountability Act underwent some pretty significant updates from 2019 to 2022 and ultimately, got quite a bit longer than its predecessor. This was necessary to clarify definitions, explain processes, reflect best practices, and decrease ambiguity. Personally, I now have a much greater understanding for why lawyers are Like That and a greater appreciation for specificity and caring where the comma goes! There can be very good reasons for legal text to be really long and wordy.
So what does “to direct the Federal Trade Commission, etc etc” actually mean?
Here’s the tl;dr: The Algorithmic Accountability Act of 2022 says that the US Federal Trade Commission (FTC), one of the Federal agencies that—as part of its mission to protect consumers—regulates how companies behave, needs to create and then enforce requirements for companies to assess the impacts of “augmented critical decision processes.”. Here’s a one-pager summarizing it, as well.
That was already a lot, so we’re going to break it down further.
An “augmented critical decision process” is a process where an “automated decision system” is used to make a “critical decision.”
What are “automated decision systems,” you say? Here’s exactly what it says in Section 2(2) (or “§2(2)” if you wanna be fancy):
The term “automated decision system” means any system, software, or process (including one derived from machine learning, statistics, or other data processing or artificial intelligence techniques and excluding passive computing infrastructure) that uses computation, the result of which serves as a basis for a decision or judgment.
In essence, these are computational systems, and in this bill, they are pretty broadly defined. This reflects research from experts like Rashida Richardson recognizing both that:
Technology evolves and definitions need to be robust against the rapid rate of change
AND
Many harmful systems are… kinda boring!
While new innovations in AI and machine learning with deep neural nets are dazzling (and sometimes terrifying!), a lot of the automation that is taking place across society is not particularly technologically advanced. Even so, automated technologies have the power to scale benefits and harms to millions of people. (This is especially true when they are used to make “critical decisions” about people’s lives!) So—even though it’s often thought of as an AI bill—the Algorithmic Accountability Act of 2022 doesn’t specifically focus on AI or particular automation techniques.
Okay, so we have a definition for an automated decision system, now what is a “critical decision?” Critical decisions are decisions relating to consumers’ access to or the cost, terms, or availability of education & vocational training, employment, essential utilities, family planning, financial services, healthcare, housing or lodging, or legal services. (We will dig into this more in Why I’m (still) hyped about the Algorithmic Accountability Act of 2022, but you might notice that there are parallels in this language to the EU AI Act’s 2021 “Annex III: High-risk AI Systems Referred To In Article 6(2)”)
So that’s what the bill says it’s about. Put all together: it’s about telling the FTC to create and then enforce requirements for companies to assess the impacts of using computational systems, the results of which serve as (or are intended to serve as) a basis for a decision or judgment about the cost, terms, or availability of a bunch of critical stuff in people’s lives like education, employment, healthcare, and housing.
That’s quite a mouthful, which is why legislative texts often define a bunch of terms to serve as a shorthand (kind of like creating variables in computer code).
In part 2 on why I’m (still) hyped, I’ll break down some of the things that I personally find most exciting, but if you want to get more context you can read the section-by-section summary of the bill for more info (or you can even read the full text if you’re into that) along with other resources linked at the bottom of this press release.
Read more:
Why I’m (still) hyped about the Algorithmic Accountability Act of 2022
"Fortunately, there was a parachute in the airplane."
Growing up, I had the pleasure of reading (or being read) Fortunately, the 1964 classic by Remy Charlip. This children’s book details the story of a boy who encounters a variety of twists and turns on his way to a birthday party. Every page of the story reveals a new “fortunate” or “unfortunate” event.
For example: Unfortunately, the airplane engine broke down. Fortunately, there was a parachute in the airplane. Unfortunately, there was a hole in the parachute.
Inspired by this story, I have been using the Fortunately format to facilitate discussions about the future of our world with the transformational impacts of technology. The simple back-and-forth alternation of sentences starting with “fortunately” or “unfortunately” provides a useful (and often entertaining) structure for exploring a potential change or scenario beyond just its first or second order effects.
An example of the output from a recent workshop on the impacts of AI.
Given the popularity of Charlip’s book and the simplicity of the format, I’m sure that others have created similar activities, but after having used this “Fortunately/Unfortunately” activity to great success in multiple foresight-oriented workshops, I was surprised to learn that none of the participants or other facilitators had experienced it before. As a result, I’ve decided to write a little explanation here as well as provide some resources for facilitating this activity below.
How to Do It
To run a “Fortunately/Unfortunately” activity, provide participants with a scenario prompt or choice of prompts based on the topic you hope to examine. These prompts should be concise and evocative. They may come from earlier scenario development as in a foresight context or may be taken from contemporary news headlines or other sources.
Some examples I used in one workshop:
The Earth is running out of helium
10MM dogs & cats are lost/stolen in the US annually
Invasive Aedes aegypti mosquitoes are spreading
5.6MM children under age 18 have food allergies
IKEA is discontinuing the BLÅHAJ 🦈
Whatever the specific prompt scenario, encourage participants to write a story using the alternating sentence format to generate as many sequential fortunate or unfortunate effects or events as they can.
This activity is fairly accessible and may be most successful in smaller groups (3-5 people) with a shorter timeframe (10-25 minutes) as part of a larger workshop or discussion. I’ve used this format successfully both in online and in-person workshops.
I’ve included a Google Slides template for facilitating your own Fortunately/Unfortunately activity.
Variations
If you’re looking for ways to experiment with this activity format, here are some modification ideas to try:
Use different categories and qualifiers to steer exploration on the same prompt (cultural, environmental, technological, etc)
Pass the fortunately/unfortunately stories between different groups and have participants pick up where the last group left off
Switch the “fortunately” or “unfortunately” preface around on one of the sentences and see how that changes things
Explore “best case scenario” and “worst case scenario” versions of the same story
This post was originally written for the Frontier Development Lab program of Trillium Technologies. Learn more about the Frontier Development Lab.
Helping the helpers: Shining light on informal settlements with satellite imagery and AI
A team of researchers from FDL Europe in partnership with UNICEF has developed first-of-their-kind tools and resources for mapping the world’s informal settlements. These tools and resources are now freely available online and can even be used on laptops by communities and aid workers in the field.
People living in informal settlements are many of the world’s poorest people. They lack access to essential infrastructure such as electricity and clean water, often while being exposed to dangerous environmental conditions. Addressing the needs of people living in informal settlements is critical to achieving several of the United Nations sustainable development goals, but gathering the necessary information to serve these vulnerable communities has historically been difficult.
Previous efforts to study informal settlements with machine learning and satellite imagery focused only on specific regions or relied on very-high resolution (VHR) imagery (down to 30cm per pixel) which can be expensive not only to access but also to work with. Through active partnership with UNICEF, the FDL team devised a method for using freely available, but lower resolution multi-spectral imagery (ranging 100cm or 200cm per pixel) from the Sentinel-2 mission. With this, the team developed a cost-effective and more user-friendly method for detecting and mapping informal settlements around the world by spectral signatures.
This cost-effective approach leverages Canonical Correlation Forests (CCFs) which, in addition to being computationally efficient, are data efficient meaning that they are a strategic choice for the sparse ground truth datasets that exist for informal settlement research. In addition to the cost-effective CCF approach, the FDL Europe research team also developed a more computationally intensive Convolutional Neural Network method to work with VHR imagery that can detect contextual features. This is sometimes necessary for distinguishing between formal and informal settlements where spectral data is not sufficient to differentiate due to identical building materials or other factors.
One of the prevailing challenges of developing informal settlement mapping techniques with satellite imagery is the scarcity and inaccessibility of ground truth data which can sometimes be locked away in PDF or even less accessible formats. To accompany the two mapping methods that the FDL Europe research team has produced, the team has also composed a series of annotated ground truth datasets and published the first ever benchmarks for detecting informal settlements.
All of these methods, datasets, benchmarks, and further information can be found for free on the Frontier Development Lab website.
As the year comes to a close, it's a time we're often drawn to look back and reflect. Using a simple form I created, I gathered anonymous feedback and advice from over 25 friends, mentors, and colleagues. I learned a lot about myself (and my community) and want to share this opportunity with others. You can learn more about how I used the form, what I learned, and how to send your own below!
Quick Links:
Demo Form - an example showing how I personalized the format
Copyable Template - for you to customize and get feedback from your own community
What I Learned - details on the process and what I learned from my 2021 survey
Feedback Request Template - for asking your friends, mentors, and colleagues for input
Context
This year, I decided to dive into something new. I chose to take a risk and pursue an opportunity to learn and contribute my expertise to critical policy decisions in the US Congress. To do this, I quit my job, moved across the country, and committed to spending a year on the Hill without knowing what would come next. Stepping into government service has been a tremendous learning experience (more on that soon!), but this time-limited opportunity also provided me an occasion—or a forcing function!—to evaluate what the next phase of my career post-January would look like.
This has been both thrilling and terrifying. I knew that I would need help.
I sought input from friends, mentors, and colleagues from different parts of my life to assist me in seeing myself from the outside and to gain wisdom to help me make decisions about the future. I created an anonymous feedback and advice form and sent it to just over 70 people. I sent it to folks I had worked with (formally or informally) and to people who are my trusted friends and mentors. I received responses from 26 people (~36%), and their answers both inspired and surprised me.
This was actually my second time sending out an anonymous form for gathering feedback and advice. I was originally inspired to create a form like this at the suggestion of Tim Courtney who had graciously shared with me an example of some similar feedback that he had received. The form I created in 2018 was very similar but was shared with a smaller group and wasn’t paired with as clear of an ask. Although I maintained or built upon many of the design decisions from the first iteration, there were things that I chose to change to increase the usefulness of the answers and anonymity of the survey.
My goal this time around was to create space where friends, mentors, and colleagues could reflect back how they experience my strengths and tell me where I have room to improve. I asked them to share advice on what I should pursue—as well as things to avoid!—in the next year or few.
Where to next?
I am so tremendously grateful to the many people who contributed their wisdom to this project. It was incredibly inspiring and encouraging to receive so many kind words of support from people I respect and admire. I have so much gratitude to everyone who contributed their understanding and reflections. Good advice and candid feedback is such a precious thing. To all of you who offered your thoughts: Thank you.
While I am still digesting it, I am translating this feedback into action in a couple of ways:
First, I want to grow my capacity in the areas where people see promise in me. This means honing my strategic decision-making, cultivating more entrepreneurial pursuits, and sharing both the breadth and depth of my expertise more widely.
Second, I am changing the way I talk about myself when expressing what I want and what I do. I am learning to recognize my own style of leadership and getting more comfortable expressing it as such. Being a leader can mean being a team player, and being a team player doesn’t mean you aren’t a leader.
Finally, I am doubling-down on some of my strengths in communicating, convening, and coordinating. I have sometimes worried that leaning into these strengths would lead me to be pigeonholed, but I’m coming to realize there are many ways to express these abilities and many opportunities to put them to work.
I will continue to reflect on the feedback and advice that I received and will continue to grow from the generous insights I have been able to glean. Thanks again to everyone who has made this possible.
What I learned
While it may feel a little formal, using a form like this can alleviate some of the typical challenges in getting feedback and advice. Providing feedback to friends often makes people anxious. They may feel nervous about causing hurt feelings or may be uncertain about what sort of input would be useful. The design decisions made in this survey were deliberately chosen to try to reduce some of these points of friction that can get in the way. I’ll step through each of the questions and share both the “behind-the-scenes” thinking as well as what I learned from the answers offered by my brilliant friends, mentors, and colleagues.
Take a look at the demo form or copyable template to see the specific questions and how they were included.
List of words
One of the first things you may notice about the list of traits included in the survey is that they are all positive. Providing critical feedback—especially to friends—can sometimes be difficult, so throughout the survey, generally, I tried to frame things in terms of positive terms, skills, and strengths. This way, people would likely have an easier time giving candid feedback, and I would still be able to learn from any notable omissions. (If I’m entirely honest, also, it was easier for me emotionally to ask my friends, mentors, and colleagues to tell me about my strengths than my weaknesses!)
In the first question, I asked responders to select words from a list according to which “best describe me.” By limiting responders to 12 or fewer of the 28 options, I forced people to prioritize which traits best described me, while liberating them from the anxiety of being “rude” for singling out a given word as not a good descriptor. (To the friends who texted me to complain about this constraint, I love you all, but it really was intentional!) The limit on number of selections allowed me to see which traits were notably absent. In my case, out of all 26 responses and 275 words chosen, not a single person chose “Systematic” as a word that best describes me. Good to know! "Focused" and "Patient" were also less popular choices pointing both to opportunities for improvement as well as roles and responsibilities that may, at least currently, not be a great fit.
The top words chosen to describe me (selected by at least half of responders) were: Engaging (chosen by 19 people), Inspiring (19), Collaborative (17), Authentic (17), Open-minded (16), Creative (16), Sincere (16), Giving (14), Ethical (14), Supportive (13), and Diplomatic (13). While some of these didn’t particularly surprise me, which is a vote of confidence for my own introspection, I was surprised that “Open-minded” and “Diplomatic” were as popular as they were. I’d like to think this bodes well for my current work in Congress, but could also point toward other management and mediation roles.
It’s worth noting: in the 2018 version of this survey, I allowed people to submit free-response answers as an “other” option to this prompt. This became kind of messy and resulted in duplicates as well as people adding too many words to really be useful. In the 2021 version I did away with the “other” option in order to learn more from the trends in what words were and weren’t chosen. One regret I have from my 2021 form is that, despite trying to limit the number of words and increase the relative significance of each word, I still had some redundancy. My 2021 word list included both “Authentic” and “Sincere,” which I think are similar enough that I should have chosen only one of them. The template form I’ve shared here lists only “Authentic,” but you are, of course, welcome to customize the list for your own copy.
Work experience
Another simple question I included in the form was a binary choice for the question “Have we worked together?” I did not provide any “helper text” (called “description” in Google Forms, if you're looking for it) for this question and allowed people to determine on their own terms what it meant to work together. While this pretty clearly captures colleagues with whom I had worked directly with as part of our employment, it also held space for those who had worked with me in a volunteer function or as collaborators in creative ventures or other capacities. Asking this question also allowed me to apply a filter or lens to the feedback I received. For instance, only one of the 12 responders who had not worked with me chose “Innovative” as one as a word that best describes me. By contrast at least half of the 14 people who had worked with me chose “Innovative” among my top traits.
Overall Worked with Did not work with Number of responders 26 14 12 Total words selected 275 153 122 Average words per responder 10.6 10.9 10.2 Most popular word Engaging Collaborative Inspiring Most popular word tally 19 11 9
Those who had worked with me chose slightly more words on average, which may have been a factor in why more than 70% of them chose the same top five traits: Collaborative, Engaging, Open-minded, Inspiring, and Creative. In addition to these traits, they were much more likely than those who had not worked with me to say that I was Innovative (7:1), Supportive (8:5), Diplomatic (8:5), and Entrepreneurial (4:2). For their part, those who had not worked with me were more likely to choose Adaptable (7:3) and Humble (5:3) as words that best describe me.
Shared skills
In addition to the list of 28 traits, I also provided a free response text box with the prompt “What is a skill that YOU have that you think that I also have?” I wanted to give people a chance to reflect on their own skills and to put forward ideas that may not have been captured in the original word list. I also wanted to get a little more context to their other answers. While the list of words question offered everyone an opportunity to reflect on the same prompts that I provided, I hoped this question would provide a space for people to speak on the topics they felt more qualified to appraise on.
In my 2021 survey, common themes were curiosity, community-building, and creative problem-solving. Some people chose to answer the question in just one or two words, echoing the list from the first question, while others wrote multiple sentences providing more detail or multiple answers to the question.
It’s worth noting that, while this whole exercise is, of course, biased based on who you invite to give feedback, this section in particular has two layers of bias. This is because it includes an appraisal both of the responder’s own skills and yours. In the future I might remove this section because I’m not sure how much additional value it provides. I was hoping to give people a chance to reflect positively on themselves, not just on me, but it’s possible this triggered more insecurities than positive feelings. Perhaps a future iteration will replace this with a question more like “are there any words you feel were missing from the list?”
Advice, next steps, & lessons to learn from
The answers to the next three free-response questions made up the bulk of the feedback. In each one, I tried to capture a slightly different angle on the advice I was looking for: what am I already doing that I should do more of, what am I not yet doing that I should be doing, and what should I try to avoid? While it’s possible that these questions could have been collapsed into just one or two, I think that splitting them out creates more opportunities to gain valuable wisdom and advice. That said, if you expect the length of the form to be a hindrance for getting a robust and diverse enough number of responses, I recommend consolidating.
The first question asked “What is something you've seen me do that you wish I'd do more of?” This was building off of the prior questions about skills, traits, and strengths. This also gave people an opportunity to shift from just reflecting into more active advising. I used this question to try to encourage specific examples of behaviors or activities I should double-down on in the eyes of my friends, mentors, and colleagues. This also provided space for examples of things that I may not yet be good at, but could develop.
Before the final two questions, I included a little more specific information about what kinds of advice I was seeking. I used the space to state some of my interests (“climate action, disability justice, and technology”) and to clarify some of my goals and ambitions (“to pursue big beautiful challenges”). In the last two questions, I also added "helper text" to provide additional commentary and context.
The second question I asked was “What should I be trying to do next? What do you think I could be good at?” accompanied by the additional questions “What job roles might suit me? Fields I should explore? Are there certifications or training that I should consider?” I provided these more specific questions as examples so that responders would know the right level of detail and specificity to provide in their responses.
Similarly, for the last question, “Is there a lesson you've learned in life that I can learn from?” I added “Pitfalls that I might be prone to? Things that are too easy or may box me in? Things you wish someone would've told you?” The reason that I framed this question in terms of the responder’s own experience is similar to the reasoning on shared skills. I wanted to give people permission to share some hard truths, but I also wanted people to reflect and draw upon their own experiences.
While I am still digesting all of the incredible feedback I received and there are still more themes to pull out of it, here are three high-level themes I noticed that surprised me:
People see me as a leader (or want me to be more of one).
I tend to think of myself as a team player and don't always feel certain enough to be the executive decision-maker. Despite this, many people suggested I pursue more management and leadership roles, especially leading multidisciplinary teams. Multiple people even suggested I run for public office! I do love connecting people and collaborative work, so I am going to try to lean into this more in the next few years by developing my ability to coach, make strategic decisions, and to overcome my insecurities about calling myself a leader.
Cultural and value alignment are a real concerns.
Several people expressed concern about me pursuing roles in organizations that would lead me to feeling burnt out due to bad organizational culture or poor alignment with my values. Some people expressed this by suggesting entrepreneurship or leadership roles (“become an owner as quickly as possible”), while others emphasized “knowing when to quit” and to not pour too much of myself into trying to change toxic workplaces in favor of moving on to healthier, more value-aligned environments. I like to joke that I chose my current team and boss based on “vibe,” but it's kinda the truth. I have worked to recognize how important feeling valued and value-aligned is to me doing my best work. It was heartening that multiple people encouraged me to not lose my funky individuality and sense of purpose and to seek environments and community where that can thrive.
I can afford to fail more and take bigger risks.
Given that taking the leap to pursue my current work already felt like a risk, I confess this theme was a little tough to internalize. That said, (and maybe because of that feeling) I think this is the perfect time to get this feedback. In many ways, the next steps from my current position may seem “obvious.” Obviously, I should keep working in AI policy. Obviously, I should leverage this fellowship as a foot in the door to being a Congressional staffer. Or obviously, I should pursue a policy role in a tech company or tech-oriented non-profit. And maybe I will! But the universe of possibilities is wider than that, and I have not been as ambitious as maybe I could be because I’ve been nervous about being ready. The advice I received is that sometimes you just have to leap and do things before you're ready in order to become ready.
Anonymity
In the 2018 version of this survey, I gave people an opportunity to relinquish their anonymity and provide contact information to discuss their advice and reflections further. In the 2021 edition, I removed this option. What I found in the previous iteration (which I admittedly sent to a smaller group of people) was that if enough people chose to identify themselves, it essentially outed anyone who didn’t choose to.
I feel like there is value in the anonymity, not only for the responders but also for myself as I read the responses and interpret the feedback. For this reason, I decided to remove the field prompting people to share their contact info. Despite this, some people did share their names and contact information in other areas of the form. This was fine given the low stakes of this survey, but if I want to survey people anonymously in the future, I may explicitly ask people to not identify themselves.
Feedback Request Template
This kind of feedback is deeply personal, so it’s great if you can personalize the feedback requests you send (or at least use a tool like mail-merge). Still, if you’re sending to a larger group of people (or a couple different groups), it can be helpful to start with a template.
Your request message is an opportunity to connect and set the tone of the feedback you want. I typically use it as a place to be reflective and vulnerable. You might use it as a place to express gratitude for the mentorship you've received or to shine extra light on the goals you have for getting this feedback.
I’ve included two feedback request messages here. One is a more generic template message illustrating some of the components you may want to include. The other is the text of the request that I sent out. As you can see, these messages can vary quite a bit based on your audience and the tone you want to convey.
Message template
Dear friend,
I'm sending you this note because I would appreciate your feedback and advice.
I am [coming up on some milestone (it can be something work-related or not)], and I want to take some time to reflect on where I am and where I’m headed. I would be grateful if you would take a few moments to answer a couple of questions and give me some perspective on what you think I’m good at and what I should be thinking about doing in the future.
Here is the link to provide anonymous feedback: https://forms.gle/7E7droUi9SpbaFRH6
I’d appreciate if you could share your thoughts by [a date two weeks from now].
Thank you!
The message I actually sent
Hello, friend!
I'm sending you this note because I would appreciate your [anonymous] perspective on, well... me!
Just a few months remain in my fellowship working in the US Senate, and it has been an incredible experience. I may not know what I'll be doing after January, yet, but I do know that I am blessed with an incredible community of friends, colleagues, and allies, and I'm so grateful for you all pushing me, lifting me, and supporting me in being my best self.
It's a curious thing, isn't it? A "best" self.
While there are many flaws and tragedies in the history of America, there is one thing that has long stuck with me from this line in the Preamble of the Constitution. It opens with the phrase "in Order to form a more perfect Union..." The idea of a "more perfect" anything is such an interesting one. The idea of not achieving or even aiming to achieve perfection but instead striving for a state of perpetually better striving is something that is deeply relatable to me.
I would be so grateful if you would take a few moments to help me in my own process of perpetual striving to be a more perfect B.
Here is the link to provide anonymous feedback: https://forms.gle/KXcpHF1kbR6po39Q8
Thank you!
This post showcases the results of the #DrawingEvolutionGame. For more on the process and to get resources and templates to coordinate a game of your own, check out How to Run a #DrawingEvolutionGame!
From humble beginnings, our plucky little adventurers grew. Transformed by many hands, through many evolutions, they became glorious boss-battle-worthy heroes!
The path to greatness for each of our heroes took a series of twists as 24 different artists took their turns to making each evolution more powerful and fabulous than the last! What follows are the incredible creations of a multi-artist collaboration that spanned three months, multiple mediums, and many timezones.
Our game started with three different seed characters drawn by Ellis Kim (timefiddler), Juanita Crooks (yakisobababy), and myself along with a simple prompt:
Together, we will take a couple of "low level" seed characters and through each consecutive artist's work, we will "level up" these characters with illustration.
Imagine these characters are starter Pokemon or freshly generated level 1 characters in a video game. In each drawing, they should become stronger, have better items/equipment/armor, or get more powerful or advanced. Their journeys should show progression and continuity from one level to the next. It's important that each successive evolution builds on the last while still leaving room to grow so that the next artist can add their rendition.
Each seed branched into two entirely independent evolutionary lines resulting in some very different outcomes!
There was such an enthusiastic response to the game that several of the artists stepped up to contribute multiple drawings. With the three seeds splitting into six branches, there were enough enthusiastic artists to produce seven generations of evolutions, totaling nearly 40 drawings!
Click on the pictures to see them larger
In order to produce these incredible creations, the different artists used a variety of tools and media including Procreate (~33%), traditional art (~30%), Photoshop (~30%), Clip Studio (~21%), and more, often in combination! For most of the artists (~80%) this was their first time participating in a collaborative art game, but pretty much everyone plans on not making it their last.
I have so much gratitude to all of the extraordinary artists who contributed and collaborated to make this possible. If you were part of making this possible, I am so grateful to you for your creativity, patience, and willingness to experiment. You make magic happen. Thank you.
The artists (in order of appearance):
Artist Twitter Instagram Facebook B b_cavello b_cavello Ellis timefiddler timefiddler Juanita yakisobababy Alec iAMKonoSnave iAMKonoSnave Jo joronimoh Greg GregSlagel GregSlagel Ashley cranberryofdoom Joy jojostoryart jojostoryart Jojostory Emily megamoth mega_moth Emily C. Martin Ro shotrohi ro.higashi RoHigashi Debbie Debzzz39 Jimmy Rachel rachel_liddell Spencer ScBingham Max Ailís ailis1991 ailis1991 Ailís’s Wonderland Studio Matt Stilltsinc stillts Susie susietothai Yvonne necroprancer firanul Raph raphdamico Jacquelyn jacquelyn_zehner_printmaker Jacquelyn Zehner Printmaker Jes Alex fancy_chelini fancy_chelini Jamie yojambo yojamboo
Feeling inspired to run your own #DrawingEvolutionGame?
Check out How to Run a #DrawingEvolutionGame for more about the process behind-the-scenes and to get resources and templates to create a #DrawingEvolutionGame of your own!
Back in July, I saw two very inspiring tweets. As @_DeadSlug_ and @suizilla described on Twitter:
“We made two teams and started from one design, each artist had to make the character evolve (stronger and stronger). But we could only see the design of the artist before us.”
As a lover of collaborative drawing games like Monsterland’s exquisite corpse creations, this type of art is particularly exciting to me. I love the conversation between the different artists, each expanding on and amplifying themes from the last.
I was so inspired by those fabulous tweets, I decided to create a #DrawingEvolutionGame of my own!
Keep reading for more on the process of pulling off this feat...
or hop down to the Coordination resources below for a list of resources and templates to coordinate a #DrawingEvolutionGame yourself!
Head over to 24 artists, 39 evolutions: #DrawingEvolutionGame to see the final results!
The Set Up
To make my own #DrawingEvolutionGame, I started reaching out to artist friends asking if they would be interested in something like this. One of my friends Ellis Kim, who is a professional artist, extended the invited to a bunch of amazing professional artist-friends of his as well, and we started picking up steam.
Together, we will take a couple of "low level" seed characters and through each consecutive artist's work, we will "level up" these characters with illustration.
Imagine these characters are starter Pokemon or freshly generated level 1 characters in a video game. In each drawing, they should become stronger, have better items/equipment/armor, or get more powerful or advanced. Their journeys should show progression and continuity from one level to the next. It's important that each successive evolution builds on the last while still leaving room to grow so that the next artist can add their rendition.
The initial invite list was over 30 people long! This was amazing, but I have run enough events (and cancelled my fair share of plans) to know that people don’t always show up, especially when committing to do something several weeks in advance without knowing what their lives might look like by then. To help account for this, I strategized a sign-up system and flexible plan to help prune commitments from those who weren’t up to the challenge and to make accommodations for those whose commitments were interrupted by life.
The sign-up questionnaire was essential both as a barrier to entry to gauge who was serious and because I didn’t know everyone personally, I wanted to make sure that people were clear on what they were signing up for and wouldn’t spoil the game accidentally.
The sign-up questionnaire had three major goals:
Making sure people who signed up were really on-board and could choose how much drawing they wanted to commit to
Collecting vital contact information to facilitate the exchange of drawings
Communicating the rules of the game (white or clear background, no posting until the game is done, agreeing to having their work be shared in compilations)
See what the questionnaire looked like!
Note: you can’t submit to this form, this is just a preview for your reference, if you’d like to have your own copy of a form like this, scroll to the end of this post!
Originally, I thought that I would create one seed drawing and that Ellis (timefiddler) would create one other, but overwhelmed by the amount of enthusiasm about participating, we decided invite a third artist, the talented Juanita Crooks (yakisobababy), to create an additional seed character.
Even with the sign-up questionnaire as a threshold for commitment, life does happen, and I still expected some drop-outs. To try to limit the impacts of people dropping out, I limited the time each artist would have to work on their contribution to just one week before they’d need to pass their work forward. The three seed drawings meant we would have six branches (two separate branches per seed) which could run in parallel. The parallelization and short time limits helped to ensure that artists wouldn’t have to wait too long before their turn, and if anyone dropped out, it wouldn't be too disruptive.
Of the 30+ artists who had expressed interest, 25 made it through the sign-up questionnaire. Expecting some drop-outs, I targeted five to six evolutions on each (total of 27-33 drawings). Miraculously—despite the size of the group—we only had one drop-out resulting in six branches totaling nearly 40 drawings!
Check out 24 artists, 39 evolutions: #DrawingEvolutionGame to see the final results!
Coordination resources
In order to coordinate this many people, I used a combination of spreadsheets, Google forms, and mail merge. I won’t pretend that it was easy, but doing this made it more manageable. Hopefully these resources can make that process easier for you.
Here are the basic steps:
Recruit a team (or few)
Create the “seed” character(s)
Send the character to the next artist (mail merge email template below)
Evolve the character to its “next level”
Send the evolved character to the next artist (don't reveal prior steps!)
Repeat steps 4 & 5 until artists have taken a turn and the character is in its FINAL FORM
Share your masterpiece with the hashtag #DrawingEvolutionGame
Resources to create your own:
Sign-up questionnaire: example / make your own
Close-out questionnaire: example / make your own
Mail merge spreadsheet template: preview / make a copy
Mail merge email template:
Hey there, {{New Player}}!
Your evolution game inspiration drawing is coming from {{Last Player}}. Reply back to me here saying "received!" or something like that when you have received your inspiration from {{Last Pronoun}} (or if you haven't received it after tomorrow, let me know).
REMINDER: {{Last Player}}, do NOT send your drawing to me. Only send it to {{New Player}} at {{Email address}}. (At the end, everyone will upload their drawings into a shared folder and I'll collage them together.)
{{New Player}}, you have until {{Deadline}} to complete your drawing at which point you'll be sending it along to the next player.
Keep in mind:
- To standardize, characters should be drawn on white or transparent backgrounds and saved in JPG, GIF, or PNG file format (We welcome physical media for creation, but please be sure to clean up and save in one of these formats for the final!)
- Each artist will see only the drawing directly ahead of their own in the evolution, so don't post images of your work until the game is complete
- It's important that each successive evolution builds on the last while still leaving room to grow so that the next artist can add their rendition
Let me know if you have any questions.
Good luck!
I hope you have a fantastic time creating and evolving your character drawings with friends. I can't wait to see the #DrawingEvolutionGame masterpieces you will create!
See 24 artists, 39 evolutions: #DrawingEvolutionGame for the grand finale!
In the summer of 2020, in the midst of the great tragedy and uncertainly of a global pandemic, I applied to participate in TechCongress. I was one of six technologists honored with the opportunity to serve in the US Congress for the 2021 year as a Congressional Innovation Fellow. Experiencing up-close-and-personal the many challenges and opportunities of developing tech policy in the US Senate has been an extraordinary experience, and the (still growing) TechCongress community continues to inspire me. Read more about what inspired me to work in Congress.
Technology touches virtually all areas of life and every issue before Congress. TechCongress gives talented technologists the opportunity to gain first-hand experience in federal policymaking and shape the future of tech policy through our fellowships with Members of Congress and Congressional Committees.
Since I started the fellowship in January 2021, I’ve had people reach out to me interested in applying to the program. While I am only one person from a diverse cohort of Fellows, I thought it might be helpful to share what I wrote in my application for future aspiring Congressional Innovation Fellows and more. If you're interested in applying to TechCongress, I highly recommend checking out the fantastic resources and information sessions hosted by the fellowship selection team.
What follows are the unfiltered answers from my application to the 2021 Congressional Innovation Fellowship. Looking back, there are some things I would probably do differently (I'd never written a memo before in my life)! Many of these answers may be out-of-date regarding both my current skills and goals as well as the TechCongress Fellowship's application questions, but I hope they might be useful as a reference.
Application to the 2021 TechCongress Congressional Innovation Fellowship
Show off your skills! You can add up to three website links including LinkedIn, GitHub, a portfolio website, or anything else that showcases your abilities.
Please list or describe your technical skills and any relevant training
IBM Certified Watson Application Developer, Certified Design Thinking Facilitator, Artificial Intelligence and Machine Learning (AI/ML) subject matter expert, Public Education, Community Building, Cybersecurity, Public Speaking, Leadership, Project Management.
I am a dabbler in a variety of programming languages, but find myself doing more high level project management and strategy work.
Name three Members of Congress you think you might like to work for
Lloyd Doggett, Joaquin Castro, Elizabeth Warren
Rapid changes in technology have created a number of challenges for Congress. You are working in a Senator’s office and have been asked to write a memo about a technology policy issue. Remarkably, the issue you've been asked to write about is an issue that you have experience with, but in which the Senator and his or her staff have very little expertise. (You can also pick an issue that you may have less experience with but that you find interesting and think is important for Congress.) Describe 1) the nature of the problem, and its significance (while taking care to explain any complex technical concepts) and 2) your solution and recommended course of action to advance your solution
TOPIC:
Facial Recognition research threatens US foreign policy interests in China and elsewhere
BACKGROUND:
Facial Recognition software is a technology that enables automated identification and categorization of people based on visually apparent features like phenotype or individual unique digital “face prints.” Facial recognition is a rapidly advancing technology despite requiring large amounts of facial image data to produce and poor performance on some populations (such as women and people with darker skin). It is a field that has drawn a lot of attention and research investment including by our military.
The most common application of facial recognition software is for surveillance. In China, this technology has been deployed to commit human rights abuses against Uyghur muslim ethnic minority in the western Xinjiang region where hundreds of thousands of people are estimated to be held in detention camps and many more are tracked with intrusive surveillance.
ISSUE:
US research funding—including from military branches such as the Air Force and Navy—has been connected to research collaborations with Chinese state-sponsored companies implicated in human rights abuses in the Xinjiang region. The links to abuse in China are increasingly well-documented, so in the last two years, restrictive actions have been taken including additions to the US Dept of Commerce Entity List for known specific actors, but facial recognition research investments continue to pose a foreign policy threat.
Additionally, connections between US-funded facial recognition research and oppressive regimes elsewhere remain poorly understood and present both a foreign policy risk and strategic misallocation of resources.
RECOMMENDATIONS:
1) INVESTIGATE the connections between foreign (state and non-state) research partnerships and US government funding in surveillance technologies such as facial recognition to identify additional sources of conflict.
2) MANDATE risk assessment statement in all government-funded research that acknowledges both misuse and failure modes as potential dangers.
3) RECOMMEND a halt to government-funded facial recognition research due to the strategic risks it presents.
Explain your reasons for wanting to be a Congressional Innovation Fellow. How will the fellowship 1) improve your capacity to effect change and 2) fit with your career plans?
So far in my career, I’ve worked in literal garage startups and massive multinational corporations, in academic environments and in nonprofits. All of this experience has reaffirmed to me that it is essential to the well-being of our country and our planet that our government have both the expertise and the capacity for tech-informed leadership. I don’t want to sit by the sidelines and wish that our government was better equipped to address the fast-moving issues that technology sparks. I want to be part of the solution.
I want to better understand the realities that congresspeople face and the opportunities to affect change on issues that are important to me: health & disability justice, security & privacy, shared prosperity, and our environment. I know that firsthand experience working in government will help me drive positive change and inform my decision on where my abilities can do the most good.
Tell us about your qualifications for the Congressional Innovation Fellowship. Think about highlighting how your strengths and experience — including technical knowledge and skills — will help you to succeed in the program and in Congress.
I work as a program lead in a multistakeholder nonprofit with many of the biggest names in tech, academia, and civil society. My specialty is translating between contexts and facilitating important conversations across philosophical and professional divides. My research specifically centers on the impacts of technology on society including issues like automation & employment and how artificial intelligence (AI) may present risks including making it easy to create and spread misinformation.
I bring to this work my experience working at IBM in the AI implementations division, where I advised international corporate and government leaders on the impacts of technology. There, I also founded and led an educational group, upskilling >40 colleagues (designers to data scientists) on deep learning technology.
A community-builder at heart, I’m eager to contribute my skills to the civic leadership of this country and look forward to facilitating new connections between government and the communities we serve.
This post was originally shared on the TechCongress blog under the title Meet the Fellows 2021: B Cavello. To apply to TechCongress, visit techcongress.io/apply. You can also read B's answers to the 2021 application as a reference.
In many ways, working for the government on tech policy issues seems like a far-off fantasy that I never would have imagined becoming real. In other ways, it feels almost like fate.
My career up until joining TechCongress has taken many turns. I’ve worked in literal garage startups and massive multinational corporations, in academic environments and in nonprofits. I’ve had the privilege of working alongside countless brilliant people advancing work on important, world-changing issues. I’m grateful to have been afforded the opportunity to peer behind the curtain at the inner workings of so many of the different systems that power our world. And yet, there’s been a massive gap in my experience: one of the most powerful institutions of them all, the United States government.
If I’m honest, I’d all but written off government work. Sure, I’ve known friends with ambitions of making their way into an executive branch appointment or public defenders toiling for justice, but I never imagined a place like Congress had a place for me. As a nonbinary person, a person without advanced degrees, a person who worked at a company with “Exploding” in the name (let alone “Exploding Kittens!”), I had somewhat unconsciously come to think of Congress as outside the bounds of possibility for me.
At the same time, it also feels logical that I would be here, doing this work.
After all, Phenomenon Media 501c3, the tech education nonprofit I co-founded right after finishing university was in many ways inspired by Congress. The infamous “series of tubes” comments from 2006 had served as a wake-up call that this nation needed greater literacy around tech issues and had fueled my dive into learning about internet infrastructure and information security. Working on developing toys and tools to teach people about technology in new ways gave me opportunities to relate sometimes abstract tech topics to people’s real lives and experiences and to foster communities in the process.
Over a decade after the “series of tubes,” the notorious “Senator, we run ads” testimony from 2018 further fueled my drive to play a positive role in technology governance. Working in IBM Watson implementations gave me insight into the many challenges that companies and governments alike face in adapting to technological change well as the potential opportunities and harms of AI. As a program lead at the Partnership on AI I got to apply these insights, leading multi-stakeholder research with leaders at Google, Microsoft, Facebook, the ACLU, UNI Global Union, and others. These collaborations developed my experience translating between contexts and facilitating important conversations across philosophical and professional divides. So many of these conversations pointed back to Congress. Even with so many good-hearted people who want technology to be better and to do better, I frequently encountered frustrations that our government was not acting quickly enough or strategically enough when it came to tech policy issues. It almost became a joke that the end of every conversation would conclude that “really, it’s up to policymakers” to take action and define the landscape for responsible technology development.
All of these experiences reaffirmed to me that it is essential to the well-being of our country (and our planet!) that our government has both the expertise and the capacity for tech-informed leadership. That’s why I was so excited to learn about and ultimately have the opportunity to take part in the Congressional Innovation Fellowship. Because, thanks to TechCongress, there is a place in Congress for people with experiences like mine. I’m eager to better understand the realities that congresspeople face and affect change on issues that are important to me: health & disability justice, security & privacy, shared prosperity, and our environment.
Given my circuitous career path, I don’t know what the future will hold. However, I do know that firsthand experience working in government will help me drive positive change and inform my decision on where my abilities can do the most good. I am not going to sit by the sidelines and wish that our government was better equipped to address the fast-moving issues that technology sparks. With TechCongress, I am going to be part of the solution.
Two years ago, in August of 2018, I applied to participate in the Harvard-MIT Assembly Fellowship Program. I was honored to be selected as a 2019 Fellow and am incredibly grateful for the experience I had working with my cohort of absolutely extraordinary thinkers, builders, and change-makers. Assembly was a life-changing experience for me, and I’m fortunate to call my fellow Assemblers friends and collaborators to this day.
THE 2019 ASSEMBLY COHORT confronted emerging problems related to the ethics and governance of artificial intelligence. The cohort is a diverse group of seventeen participants who come from the private sector, academia, civil society, and government; and bring expertise in communications, ethics, machine learning, media theory, policy, project management, and more.
You can learn more about the 2019 Assembly Fellows and our research projects on the Assembly Program website.
Since my fellowship, I’ve had many people reach out to me asking about my experience and frequently wondering about how they might apply to participate. While I am only one person from a diverse cohort of Fellows, I thought it might be helpful to share what I wrote in my application for future aspiring Assemblers and more.
What follows are the unfiltered answers from my application to the 2019 Assembly Program. Many of these answers may be out-of-date regarding both my current skills and goals as well as the Assembly Program’s application questions, but I hope they might be useful as a reference.
Note: In light of the global pandemic, the Assembly Program will not be accepting new applications for fellowship for 2021, but instead “will support the growth of existing projects from the previous four years of the Assembly Program, and plans to open the call for a new class of Assembly Fellows in Fall 2021.”
Application to the The Berkman Klein Center's 2019 Assembly
What expertise or skill set you aim to contribute to the program and your team? *
[x] Communications
[_] Data science
[_] Development
[x] Entrepreneurship
[_] Ethics
[x] Machine learning
[x] Organizational / project management
[_] Policy / law
[x] Other:
Briefly elaborate on your skill set, specifying how it would help you contribute to the work of your Assembly team. *
If I had to describe my superpower, it would be creating incredibly excellent analogies. I serve as a connector and translator between different people, disciplines, and contexts.
If you are a machine learning engineer or full-stack/front-end/back-end engineer, include a link (if possible) that showcases your work: i.e. an app you’ve built, your Github page.
I'm not an engineer, but I've been learning (https://github.com/bellabie)
1. Tell us about your experience working in a team. What was the context? What did your team accomplish? What was the most challenging aspect of working on the team? If you identify as a developer, tell us about a time that you worked with someone who was less experienced and/or less technical. *
In late 2017, I worked with a group of folks at the Debug Politics social good hackathon to create projects that would promote a better environment for online discussions and more reliable information sharing. There were many talented people including several AI developers there, and I felt a strong personal struggle choosing how to spend that time.
For my own career, it seemed most advantageous to work with and learn from the AI practitioners who were more advanced than myself, but in talking with them I was concerned that that might mean creating something with limited real-world impact within the short timeframe of the hackathon. Alternatively, I could pursue a much less sexy but more immediately applicable project idea that could be completed in the allotted 48 hours to solve a real known problem. In the end, I recruited a more diverse team around our developer's idea to create a browser extension to prompt users (very simply, without AI) with nonviolent communication and other methods to de-escalate online arguments.
Our scrappy little team consisted of just four people: Stephen Cataldo, our main developer who is a political author and drupal developer; Katie Fleeman, who did much of the writing for the project as well as design research-style interviews and works as an editor for Knowable Magazine; Senay Yakut, a rising junior web developer who unfortunately could only stay part of the time; and myself. My own roles involved project management and team motivation as well as web design and pairing to help with javascript development. Although our project attracted attention from folks interested in the more buzzworthy technologies, I fought to keep our scope achievable and to compellingly articulate the explanation for our choices. I wanted to honor how our team had forgone the hype to build something that real people could use to make the world better.
By the end of the hackathon, we had created a working Chrome browser extension to help users write better Facebook comments by prompting them with de-escalating phrases, allowing them to offload some of the emotional labor of using these communication techniques. Although this achievement may seem technologically small, I'm quite proud of it, and—because we kept our laser-focus on a working solution to a known problem—it ended up winning us first place in the hackathon overall!
2. What idea or project are you interested in exploring for this program, related to the ethics and governance of artificial intelligence? Your project proposal could address a technical problem, policy question, or some combination of the two. Project proposals should not be limited to purely technical solutions. (Be aware that the cohort will not necessarily work on this project).
I'm incredibly inspired by work that helps elucidate the workings of technology and engages more people in its design and application. Projects like last year's AI in the Loop or Distill Pub represent to me crucial forces in a movement to ensure that AI is both accessible and inclusive.
Inspired by work that I started with some local Girl Scouts, I would like to pursue a project that breaks down concepts like "what is data?" and empowers people who do not have deep familiarity with computer systems to explore and understand the issues and opportunities that exist in AI. With my background in game design, I'm especially interested in interactive activities either for individual, self-guided experiences or even group classroom-style use.
Previously, I worked on a project called colorCODE that involved creating a Montessori-style toy for teaching code. (bcavello.com/work/phenomenon/colorcode) It was received remarkably well by the families I had the opportunity to work with. I am especially excited to work with a team to think of creative ways to make resources available with accessibility in mind, whether they be interactive "explorables" accessed on the computer or offline activities.
As AI becomes ubiquitous, now it's more important than ever before that everyone who interacts with this technology have an awareness of its presence and the implications it has on their lives. I hope that the 2019 Assembly cohort and I can create the analyses and the tools that empower people in the era of AI.
Several years ago, I was inspired to write a series of tips based on my experiences navigating Los Angeles as a young person with ambitions of changing the world. In my writing, I’ve often found myself adopting various voices and personas separate from myself. For this series, I created a character I called “the good girl” to write relatively innocuous but somewhat precocious advice that I wished I could have received.
Below is a sample of the writing from this project. I’ve included here the Preface and a Guide to Ending Conversations, but I also wrote about getting dressed (and the importance of comfortable shoes) as well as tips for handling business cards and how to navigate alcohol at events when you aren’t of legal drinking age. (This is, after all, a good girl’s guide.)
Ultimately, I never finished the project. I’m not sure why entirely, but I think that it was at least in part because I was worried I wasn’t qualified to give such advice. Looking back, I wish I’d written more of them. They were tremendous fun, and now I have a little more experience to back up my recommendations. I hope you’ll enjoy!
Preface
As a budding professional, a young lady may find herself in foreign territories faced with unprecedented challenges as well as unforeseen opportunities. This brave new world is burgeoning with unmentioned and sometimes bizarre rituals and rules. Navigating (and sometimes subverting) these standards is essential for getting a foot in the door to the most powerful parts of the world. This guide is intended as a companion for up-and-coming trend-setters and world-changers who are navigating networking and weird grown-up stuff in their quest for world domination or, you know, a rockin' story to tell.
Some of the stories in this guide may include material that is dangerous, illegal, or both! As such: All characters appearing in this work are fictitious. Any resemblance to real persons, living or dead, is purely coincidental. Readers are urged to use caution and their brilliant brains if emulating any of the actions depicted herein.
With that! What I goofed, what I wish I knew, and what I know now: a good girl's guide.
Good Girl's Guide to Ending Conversations
When it comes to networking, talking to people is a must. You've got to put yourself out there to make meaningful connections and build strong relationships. Sometimes, though, it just doesn't work out. With some practice, you'll come to recognize pretty quickly when a conversation with someone isn't going to be pleasant, helpful, or both. Ending a conversation with someone, even if for a good reason, can feel rude or risky. Here are some tips for exiting conversational timesucks and saying goodbye to douchebags with grace.
Getting Out
Especially when you're a fresh face, you aren't always familiar with the who's-who of industry events and after parties. As such, you might get stuck talking with that guy who gets hammered before showing up or that over-eager sales manager who won't shut up about the new product that just shipped. Yeah, yeah, you can learn something valuable from every person you meet, but let's be real: sometimes it's just not worth the time. You'll need to learn how to excuse yourself these conversations. Think of it this way: the valuable lesson you can learn from these people is how to say "no."
It should go without saying that explicitly expressing disinterest in someone is a generally a bad move. This strategy often doesn't actually get the point across to your conversational captor, and it may come back to haunt you. You want to leave someone hanging, wanting more, but avoid giving the impression that it's personal, if possible. While you do want to exude the impression that your time and attention are valuable, if you act like a snob about it, you won't make many friends. A little sweetness goes a long way.
Instead, an appropriate excuse for cutting your time short will do. Don't be afraid to interrupt if necessary, but a conversational lull or story's conclusion is the optimal place to take your leave. Depending upon your situation, there may be any number of legitimate reasons for you looking to move on. You can, of course, excuse yourself to the ladies' room or to get another drink. Be aware, however, that these are common excuses to leave a conversation and will likely communicate that desire to the person you were talking with. In some cases, this is absolutely fine. However, I've found that there are even better ways to excuse yourself.
Assuming you've done your research, you should have some idea about the hosts of the event you're attending or the guests of note. A great excuse for leaving a conversation is in order to talk to someone that everyone recognizes as important. It explains the time-sensitivity of your action and also opens the door for an introduction, on the off-chance that your Chatty Cathy knows the person in question. Lucky you! Another way you can deflect ownership of your leave is to pin it on a friend who accompanied you to the event. Rescuing a friend from an uncomfortable situation (not unlike this one) is commendable and won't raise argument. Just make sure that any reason you provide for leaving a conversation you actually (attempt to) follow through on.
Everybody Wins
I wasted a lot of time early on worrying about hurting people's feelings when I said goodbye. After all, I knew why I wanted to leave. If they knew that I found them boring, annoying, or worse, wouldn't they feel upset? The answer is: maybe. But that's no reason to shy away from standing up for your space. At the end of the day, no one wants to spend time talking with someone who isn’t really interested. If you can get over the initial discomfort, you’ll find that everyone is better off talking with the people who are interesting to and interested in them.
With that, get out there and get networking! xoxo the Good Girl
From robots self-navigating city streets to chatbots inventing their own languages, the media is rife with stories about artificial intelligence (AI) becoming ubiquitous and more powerful. To some, this marks the start of an era of leisure and plenty as promised by utopian science fiction. To others, the outlook is not so rosy. There is a growing movement of business leaders, technologists, and researchers that is seriously concerned with the rapid pace of AI progress. Some even believe it may bring about the end of humanity. Although AI could one day pose a substantial risk, it is not (yet) beyond our control. There is still time to remedy the current harms and even sidestep the most serious threats, but we cannot afford to delay in establishing technical and ethical standards and taking action on the present problems that exist.
Cause for alarm
Elon Musk is a billionaire entrepreneur and engineer known for his ambitious technology initiatives that sometimes resemble science fiction themselves. He has used his platform to voice concerns about the rate of AI developments. At SXSW in 2018, Musk warned the audience, “AI is far more dangerous than nukes.” Renowned physicists like Stephen Hawking and MIT professor Max Tegmark have also emphasized the potential dangers of AI machine learning if it becomes smarter than human beings (or superintelligent). “The real worry isn’t malevolence, but competence,” Tegmark clarifies. “A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours.”
A superintelligent AI’s goal may be originally defined by humans to be benign or even something that helps us, but unexamined objectives may result in an AI that accomplishes our stated goals but has harmful unintended consequences. Swedish philosopher and Superintelligence author Nick Bostrom highlights the issue in his famous paper clips thought experiment:
A simple example is that of a paperclip maximiser: an AI with a utility function that seeks to maximize the number of paperclips. This goal is a not too implausible test command for a new system, yet it would result in an AI willing to sacrifice the world and everyone in it, to make more paperclips. If superintelligent, the AI would be very good at converting the world into paperclips, even if it realizes its creators actually didn’t want that many paperclips – but stopping would lead to fewer paperclips, and that’s what its goal is.
There are are many documented cases of machine learning systems performing in ways that surprise their creators, subverting their goals or finding humorous (or perhaps terrifying) hacks. When we see today’s AI do strange things like supposedly "invent their own language," it is because human beings constructed the goals for the machine poorly. They did not require it maintain human readability. Without constraints placed on the machine learning system (usually in the form of penalties against a numerical score), the AI learned a more efficient means to its end. Machine learning systems are ruthless optimizers. When they learn to maximize scores in a way that no longer results in useful or human-readable outputs, we might want to pull the plug, but a sufficiently advanced AI may not let us.
Although almost everyone agrees that superintelligent AI does not yet exist, AI safety advocates like Bostrom emphasize that its emergence could happen unexpectedly. “We humans are like small children playing with a bomb,” he writes in his book. The concern is rooted in the potential for these systems to self-improve. Current machine learning capabilities cannot meaningfully modify themselves and adapt to change, but once the ability to self-enhance is achieved, advocates worry that it may accelerate at a rate that is beyond anything people can hope to keep up with. Unlike tyrants of the past, AI agents are not constrained to a human lifespan or biological needs like sleep. If intellectual dominance is achieved, humans may have little hope of ever regaining control.
Where we are now
Even today’s most cutting-edge AI still has a distance to go before it can learn to improve upon itself, let alone start harvesting humans to make into paper clips. Current AI technology is considered “narrow” meaning that it can only perform well on specifically defined data processing tasks like looking for objects in images or identifying the terms of a legal document. This means that a single AI—even one that can beat the very best humans at a particular game like chess, Jeopardy, or the notoriously-difficult-to-master Go—will fail embarrassingly when presented with a new task such as playing a different kind of game. AI does not generalize well. While there is a lot of interest in areas of research such as transfer learning (the ability to apply the learning from one task or area to another), right now AI is fairly constrained.
This does not mean that narrow AI is ineffectual. Today’s AI provides us with unprecedented abilities to interpret data and understand our world. Narrow AI already helps us to avoid traffic, translate between languages, detect fraud, and take amazing selfies. These systems excel at tasks that are often overwhelming, redundant, or downright impossible for human minds to accomplish like cross-referencing multitudinous information sources and processing millions of files in mere minutes. According to a survey by IBM, more than 70% of CEOs said that AI will play an important role in the future of their organization, and 50% of them had plans to adopt the technology by the end of this year. IBM CEO Ginni Rometty said at the Gartner Symposium last year: AI is “going to change 100 percent of jobs, 100 percent of industries, and 100 percent of professions.” We need not create superintelligent AI in order for these technologies to transform our world. In fact, they already have.
The present danger
Even if we avoid creating superintelligence, focusing on narrow AI alone is not enough to protect us. The powerful, specialized tools of narrow AI can still be incredibly dangerous. The increasing interest in machine learning systems by military organizations provides a glimpse of what may lie in store. The same multiplying force that makes modern AI a powerful tool for good can also make it a dangerous weapon. Already, we have seen how these technologies applied to policing and surveillance can produce a data-driven panopticon. While superintelligence prognosticators fear powerful AI turning against us, perhaps we should be worried about it doing exactly what it's told.
Whether by minimizing a punishing “loss function” or trying to maximize a high score, these AI agents optimize around the goals and incentives we provide them. Even with all of our high-powered deep neural networks, human beings are still responsible for defining the rules of the game: what to value and what can be optimized away. Constructing rigid rules in the face of our ever-changing, multiplicitous world will inevitably produce suboptimal outcomes. There are no silver bullets or universal golden rules. Our messy human lives require nuance, compassion, and mindful adaptation to changes of context. It follows that if we’re trying to create AI that benefits all of us and reduces harm, it is of the utmost importance that we are careful in choosing what sorts of behavior we incentivize.
We don't have to wait for the advent of superintelligent systems to see the effects of runaway algorithms and maligned incentive systems; they’re all around us! This pernicious issue with rule-making predates computers entirely. We see it in our law, our economic systems, and even our morality. One need not look further than the burgeoning global climate crisis to see the effects of a failure to incentivize behavior that values human life. Perhaps we are the paper clip optimizers.
Elusive intelligence
So why is it, given the dangers that exist even with narrow AI, that people still pursue technological superintelligence? Despite the warnings by scholars and researchers, pursuit of ever-more powerful AI capabilities shows no sign of slowing. Enthusiasts point to complex, multivariate problems that people have been unable to address on our own. Powerful, adaptable AI systems could perhaps be used in long distance space travel missions or to care for and provide companionship to people as they age. This type of behavior is considered “AI-hard,” meaning that it would require an entity or system that is able generalize, adapt, and learn. An AI like this might become a superintelligence.
Even if superintelligent AI could be achieved without its goals falling out of alignment with our own, we (as the cognitively inferior) might lose the ability to understand its reasoning. The benefit reaped in solving many AI-hard problems, by definition, comes at the expense of our ability to check the AI’s work. Perhaps worse still, there's no guarantee that such a benevolent artificial superintelligence would even be believed by people. Returning to the example of addressing global climate change, we might pour resources into answering AI-hard questions only to come to conclusions that we already “knew” but did not want to act upon. Perhaps it is not answers we seek, but rather we seek an authority greater than ourselves.
Indeed, the very definition of “true artificial intelligence” has eluded us time and time again as the goalposts keep shifting. It was once thought that mastery at the game of chess might indicate “the possibility of a mechanized thinking” as Bell Labs engineer Claude Shannon mused in the 1950 Philosophical Magazine paper “Programming a Computer for Playing Chess.” Instead, as Shannon predicted, it largely resulted in a narrowed redefinition of human-like thought.
If we are to make strategic choices about AI today and in the future, we have to better define the scope of our problem. Then we can make informed judgments about what we are willing to sacrifice in order to address it. The telltale characteristics of AI superintelligence are amorphous and subject to change. We can’t formulate very effective strategies for such a nebulous threat. We have to target that which we can define. Fortunately, there’s a difference between asking “how can we develop AI in a safe, responsible fashion” and “how can we stop an evil superintelligence that’s already out there?”
What can we do now?
AI is a powerful tool. Even it its current form, there exist numerous opportunities to improve our health, increase safety, and help us to better understand each other. There are countless greenfield opportunities to do new incredible things. However, to reap the benefits that AI can bring, humanity must develop it responsibly and humanely. All of us must hold the creators of these systems to high ethical standards and make the weaponization of AI so reprehensible and shameful that we limit its proliferation. This will slow AI development. And it should. Ultimately, we have to be willing to make some compromises to reduce the likelihood of harm and maximize our benefit. (Call it a Skynet insurance policy.)
AI research could be likened to stem cell research: it’s incredibly potent and has the potential to do tremendous good, but there are also some dangers that we are not yet prepared to address. We can learn from the standards set forth by the international community on biological research and not feed a frenzy of “progress” for the sake of “progress.” We can demand clear explanations of why capabilities are important and require demonstrations that they can be applied safely as a stipulation for funding.
In the field of narrow AI, there is plenty of room for improvement. The general public is becoming better educated about the harmful biases built into many systems that are already live in the field. In the information security industry, companies hire hackers to actively try to break their systems to help them catch vulnerabilities and to improve. It’s a kind of security quality assurance. Organizations developing AI should apply a similar quality assurance process to the ethics of AI systems (especially in high stakes decision making). People should know where AI is being used and what risks it brings. In many cases, the architects of these technologies already are aware of their shortcomings. If the low-hanging fruit are so obvious as to not warrant an external party’s scrutiny, we should do better.
One of the most powerful tools that we have available to us right now is our connectedness. Especially while the AI community is still relatively small, individual people have the ability to influence the norms of what we want to achieve and what we are willing to accept. Indeed, it is our responsibility to be critical of work that does not take into account the potential harms (superintelligent or otherwise). This doesn’t just mean reacting at the research publishing stage or once something gets media coverage. This means friends, family, and colleagues asking other tough questions about what each other is working on. This means celebrating whistleblowers for protecting us from harm. We should make it socially untenable for anyone pursuing research on unconstrained self-modifying systems or weapons projects. Ethics and integrity are critical, and we should be unabashed in upholding these ideals.
A brighter future
Ultimately, there’s a wealth of opportunity in narrow AI and a multitude of complex and meaty problems for researchers to explore. Many of the juiciest problems aren’t technological at all. We may want to build super-powered AI systems that make ethical decisions, but we don’t yet have agreement on what ethical decisions even are for normal-powered humans. With better alignment on our own goals, we may not need to create artificial general intelligence that learns and performs at a human level; we can focus on creating ethical tools that empower people to perform at a super-human level instead.
In honor of Pride Month this June, I put out a call:
I offered to sponsor a small number of mini grants of $500 to support members of the LGBT+ community.
On the submission form, I explained:
"It is open to all queer folks, and there are no limitations on the kinds of goals or projects it can be used for. Projects in all states of completion are welcome, so please don’t be shy!"
The grant application form was launched on the evening of June 2nd, 2018 and ran for almost 10 days until I closed applications prematurely on June 11th, 2018. In total, there were 948 applications and 13 additional grant funders who contributed a total of $7,750.
Together, I am proud to say, we were able to raise $10,000 to fund twenty $500 mini grants to queer teachers, students, artists, developers, activists, and parents!
2018 Pride Mini Grants by the numbers
Total submissions: 948
Total mini grants: 20
On the application form, I asked people for their name and an email to contact them by as well as their age, who they were, how they would use a $500 mini grant, and any relevant stories/posts/images to accompany their submission.
AgeAverageRangeApplicants25.514 to 58Recipients26.320 to 39.5
I used regex to search the "Who are you?" answers to estimate the number of people self-identifying in each of the following categories. Out of the full 948 applicants:
Not only are these terms not mutually exclusive, but you can see that the sum of these categories (940) is less than the full number of applicants (948), so there are additional identities not captured here or folks who chose not to identify in any of these terms in their submission.
We were able to fund a total of TWENTY grants. Read more about what the funds supported and what I learned here.
Inspired by the amazing Mini Grants for Women, I have decided to make a 2018 Pride edition. I'll be donating to a small number of projects, but I welcome others to join me. Giving directly to others can be more powerful than you might expect!
Click here for the mini grant application! (closed 11:59pm June 11th)
FAQ
Who are you?
Check out my About page!
Why are you doing this?
Honestly, I've been privileged to have the financial security recently to give to others. While I have this stability, I promised myself that I would give 10% of my income, and this is one of the ways I've chosen to do it. You can also read more about the background on this initiative and about the benefits of direct cash transfer.
We often hear this stat that "nearly half of Americans would have trouble finding $400 to pay for an emergency." Direct giving is not the solution to this problem, but it can offer much-needed stability and agency to folks who are squeezed and stretched to the limit.
Who is eligible?
I invite any queer (LGBT+) person to apply. While it is a precursor, I welcome folks to apply even if they have concerns about being "queer enough."
Is there more to it than the application form?
Nope! That's it. Just keeping it real simple. The best way to have a shot at this is to fill out the form. Due to the number of submissions, supporting materials shared by email or social media will not be considered.
Why did the submission deadline change?
I've been overjoyed (and overwhelmed) with the reach of this project. Thousands of people have shared the link and many hundreds have applied. It is no surprise that there is tremendous enthusiasm and also tremendous need in our community. I perhaps should have set a shorter deadline in the first place! Because I did not, however, I made an update to keep this within a manageable scale.
If you have other questions, email the address listed in the application form.
In January of 2018, I came across an initiative started by Cecelia (@ceciatl) on Twitter encouraging women to apply for a personally-sponsored grant.
The application form read:
This form is for a mini-grant of $1,500. It is open to all women and there are no limitations on the kinds of goals or projects it can be used for! Please take advantage of the opportunity to upload any files or information relevant to your submission- I am expecting to receive entries for projects in all states of completion so please dont be shy!
I was so inspired.
Cecelia had also included an email to field questions about getting involved. Motivated to put my privilege to work, I sent an email expressing my desire to help fund the submissions. Direct cash transfers are an incredibly powerful tool that more of us should employ.
The Recipients
Cecelia worked with me after the submissions closed, and I ultimately decided to contribute four smaller grants of $500 each.
Here are some excerpts from their submissions:
Hope
I am a former comedian who currently works as a trauma therapist. I created a non profit that uses improv comedy as a form of therapy for children suffering from trauma, acute, chronic and terminal illness.
We have held workshops in Malawi Africa with women and children suffering from illness and HIV AIDS. We recently returned from the Syrian border where we worked with traumatized, displaced refugees using improv comedy and therapeutic grief and loss group work. We plan to hold workshops on the south side of Chicago for adolescent victims of gun violence...
This grant would allow us to help with transportation for South side youth to get to and from our workshops as well as cover the cost for volunteer training for upcoming comedy tour.
Learn more about Hope's work.
Luu
I live in Medellín Colombia and this is a city that is very dangerous for women and I have been given for almost 2 years free self defense classes too many girls (cis and trans). My goal is to have a place that is fully equipped with the tools that we need to train, this will help us to improve our level and also to have a more secured clases.
[I'll use this grant for] paying for the stuff that we need to train properly.
Learn more about Luu's work.
Jill
I'm an English Professor at UMass Boston who volunteered to teach Introductory Comp at the Suffolk County House of Corrections at South Bay. We’re trying to make this a permanent program, but in the meantime I said "Let's just start and see what happens; I'll do this for free."
Every week now I am taking in handouts and they are reading, annotating, summarizing, analyzing, and imitating poems and stories and articles I bring in. They are already crushing it, even in-text citation, which drives every freshman comp student crazy. And they are doing it without computers, in exam books--those little blue books—with pencils they have to turn back in at the end of the class.
[This grant] would help me keep buying notebooks and textbooks and dictionaries for my incarcerated students, and compensate me a little for my time. More importantly, it would help me draw attention to the program from the Sheriff's office and the university, so that ultimately this program becomes a class we offer every term, paying the same salary that non-tenure track instructors make teaching comp on campus.
Learn more about Jill's work.
Meghan
I am currently working towards my PhD in Biology at Drexel University in Philadelphia. My goal is to study the impacts of climate change on the mating behaviors of American pollinators which I hope will help us better protect them from decline. If we understand how mating and reproduction in bees are affected by climate change, we can better understand total population declines.
I am lacking some of the scientific equipment I need to do the work - and am also stressed by the expense of renting a car to get to and from the field site while I am out there (about $1000 for the month) on top of paying for travel and stay.
Having my own equipment, and being able to save some money this year to be put towards my field season next year (I anticipate needing to go for three separate seasons to gather enough data) would tremendously relieve the stress I am feeling about financially supporting my own research initiatives on a PhD salary.
Learn more about Meghan's work.
What's Next?
These women are incredible. There were also so many other fantastic projects that I could not sponsor, but I'm hoping other volunteers were able to step up.
Meanwhile, Cecelia has continued this amazing work with the Open Call for Pitches from Women. You can also get involved by learning more about and supporting the original grant recipient's project or starting your own mini grant.
In honor of Pride Month in June 2018, I launched a new Mini Grant initiative of my own specifically for queer folks!
These findings were gathered for CodeScribe in response to a hypothesis formulated during Science Hack Day. It posits that “[people] do not need any prior knowledge of coding languages to develop code literacy through reading code.” This initial, cursory analysis helps to identify some of the similarities and differences in how different skill level groups respond to the different types of questions. From this, we can gain insights into how easy and intuitive it is for [English-literate] learners to read and understand code without prior experience as well as the types of foundational knowledge required.
Background on this study and CodeScribe at Science Hack Day: goo.gl/5v22iM
You can find a copy of the code survey here:
goo.gl/forms/YdcLBmwADQepxLU42
See the data for yourself:
goo.gl/JDvZe7
Analysis
Overall, the “choose the best comment” format questions were easiest for folks to answer correctly. I’m guessing that this is because they read more naturally and (thanks to the limited answer options) could be easier to “guess and check.” Given that one of the fundamental goals of CodeScribe is to help folks develop reading comprehension for code, this is very promising. The comments represent comprehension of the “why” of code above the “how.”
Although there were two questions of this “choose the best comment” type, the error rates varied. I’m understanding this to be due to the somewhat subjective nature of comments. This is to say, the options provided may not have been equally ambiguous. The code excerpts being commented upon may have included different levels of complexity, as well. What is interesting is that for question 1, (both across experience level and across learning method) the incorrect answers were consistent. For question 2, those with less experience and more informal learning tended toward different types of errors than their more experienced, formally-instructed counterparts. This provides helpful insight into the types of disambiguation that should be the subject of future lessons.
Interestingly, question 4 presented a source of confusion that may have come primarily from the question wording. The majority of answers (over 85%!) were distributed between two very similar versions of the answer. This is likely more than confusion between two choices, but in fact may be a semantic distinction in the interpretation of the question. The question asks: “In which of the following orders does the code run when the endTurn (defined under COMMENT 2) function is called?” Here, the ambiguity is in the word “run.” Some answerers treated “running” as being called (which was considered the correct answer here), while others interpreted it as completing.
Over 70% of the incorrect answers came in the form of the answer variant which corresponds to the completion interpretation of the word “run.” If we amended the question 4 wording to say “In which of the following orders does is the code called when...?” there would almost certainly be a vast improvement in performance. If even half of the respondents who had chosen the variant had clarity from the improved wording, this question would have gone from least- to the second-most-accurately answered in the group. In the future, if presenting similar questions or ordering challenges, I will use less ambiguous language or clarify that run is equivalent to being called.
Given the wording confusion in question 4, I think it’s fair to say that—content-wise—question 3 was the most difficult. This was anticipated because the formatting in the provided code was actually quite advanced. Even folks with more advanced skills were more likely to miss it. Across the board, this question type was considered the hardest and most frustrating of the questions, and it may have been a poor choice to include it in this iteration because of the stress it caused. One pretty darned experienced respondent even remarked, “I feel stupid and I’m an expert.”
Because the goal of CodeScribe is to promote reading comprehension for code, questions like this would ideally only be used as teaching moments; they are opportunities to highlight syntax equivalency where it may not be apparent. The lack of feedback in this particular form factor (the Google Form) means that, although helpful for gathering initial insights, it is a far-from-ideal mechanism for teaching. CodeScribe must be interactive and responsive to be effective for learning.
Conclusions
One promising takeaway is that—at least on average—people can sense when they’re getting something wrong. In almost every case, average confidence levels were lower for those who answered incorrectly compared to those who answered correctly. (The main exception to this was for the “Pretty darned experienced” folks on question 3, for which some participants were so confident in their incorrect answers that they outpaced the correct answers in average confidence.)
In the “Accuracy vs Confidence” rows in grey, we see that, despite having less confidence in wrong answers more often than not, the perceived accuracy or confidence was greater than actual accuracy. The one notable exception to this pattern was for the group who learned in a formal teaching setting. On average, these respondents rated their confidence lower than their performed accuracy. I wonder if this is a result of the formal learning environment. As a trend, the difference between confidence and accuracy decreased in relationship to skill level. Both the most experienced and the formal learners had the smallest difference (~5%) but the formal learners’ accuracy was nearly 20% lower as a group than the self-identified experienced coders.
Ultimately, I found these results encouraging. Although those with little to no code education did perform worse, as would be expected, they did better than random guessing (which would have been an average error of 68.75%). I believe with some word changes to question 4, they would actually have performed relatively well. At the end of the day, CodeScribe is intended as a learning tool, not a quizzing tool. As such, the next iteration will be much more interactive and attempt to compare before/after performance.
Background on this study and CodeScribe at Science Hack Day: goo.gl/5v22iM
You can find a copy of the code survey here:
goo.gl/forms/YdcLBmwADQepxLU42
See the data for yourself:
goo.gl/JDvZe7
On the weekend of October 14th & 15th, 2017, I joined 250 creative and scientific folks at Github HQ for the 8th annual Science Hack Day. It was an extraordinary experience. Not only was this volunteer-organized and -run event exceptionally enjoyable, it was also a masterclass in inclusive event design. Massive thank you to Ariel Waldman and everyone on the Science Hack Day team!
See the results and findings from this hack:
goo.gl/9f3gUA
You can find a copy of the code survey here:
goo.gl/forms/YdcLBmwADQepxLU42
See the data for yourself:
goo.gl/JDvZe7
The Birth of CodeScribe
On day one of the event, I addressed the group to propose an idea: a reading comprehension approach to learning code. The idea had come to me only days earlier when I was expressing my frustration at coding education tools. Although there are many freely available online, I’ve never managed to follow through to completion on any of their lessons. “I don’t want to be a coder,” I lamented. “I want to be code literate! Why can’t I read other people’s code and learn that way?” Exploring this thought further, I realized that I had been learning this way. This (along with a bunch googling and helpful CS-savvy friends) is how I learned to build my website, perform statistical analyses, compete in CTFs, and more.
I’m not a coder in my day-to-day activities, but I have developed a level of code literacy that enables me to have meaningful conversations about coding problems and translate between different stakeholders on projects. There are many people like me. People who do not need or want to develop a deep syntactic knowledge of code, but do want literacy: the ability to look at code and understand what it means.
The idea was to combine two needs (that for code literacy and documentation) into one solution. “Duolingo for code,” I pitched it in the pithy this-for-that fashion that has come to define Silicon Valley. Learners would review real code snippets and respond with code comments, collectively crowdsourcing documentation for open source projects. My Science Hack Day cohort responded with enthusiasm.
The first day of the hackathon was primarily used for information gathering, relationship development, and to fail fast on a variety of attempts at bringing this idea to fruition. I connected with educators, students, parents, and professional programmers. My goal was to both better understand the ecosystem of existing resources and to identify patterns in learning styles or barriers to learning. I also talked with the Github team to better understand the options for using their API to make pull requests for comment contributions and to source the code snippets.
Toward the end of the first day, a team had come together. I was joined by Jordan Hart, Erik Danford, and Sanford Barr as core members of our team. We dubbed our project CodeScribe for users’ role as narrators of code’s meaning (with a pleasant double entendre for “co-describe” the process of crowdsourcing documentation). Together, we honed in on some fundamental hypotheses to test to inform our development:
People can learn to understand code by reading snippets.
You do not need any prior knowledge of coding languages to learn in this fashion.
There is a way to automate checking comments.
People want to learn this way. AND/OR People want this skill.
Development
Through a series of thought experiments as well as a few quick-and-dirty prototypes, we arrived at some early discoveries. We challenged ourselves to perform the tasks we would be asking of our learners, to read and comment foreign code. Our initial learning was regarding hypothesis #3. We found that, beyond creating an automated way of checking quality of comments (or “translations”), we first had to define what a good comment was. We arrived at the conclusion that a good comment communicates the “why” of the code, rather than the “how.” While, I stand by this conclusion, it did present challenges for other aspects of our plan.
My initial vision for CodeScribe had been very much like the language-learning app Duolingo: short snippets to be translated into natural language. We were enthusiastic believing that writing comments would make the mobile app interface easy because we wouldn’t run into the issues around spellcheck for typing code. Our revelation about comments, however, meant that the length of code snippets presented to the user needed to be much longer. Determining the purpose of the code relies on context.
With continuous feedback from other Science Hack Day participants, we felt relatively confident in our fourth hypothesis. At the very least, the concept appealed to people. So, with that, it came time to test my boldest, most controversial hypothesis: that people do not need any prior knowledge of coding languages to develop code literacy through reading code. My compatriots (all of whom had studied, practiced, or even taught computer programming) were unsettled by this idea. My theory was that code was, after all, created to be useful to humans and therefore is arranged and named in a somewhat logical fashion. “For English speakers,” I proposed, “many of the terms should be familiar and may even read naturally.”
To truly test this, however, we would need to try it out with actual people! This became the focus of the rest of our time at Science Hack Day. Our final project resulted in a Google Form. We used a modified program from Jordan’s own lesson plan as the sample code and wrote up four different questions to evaluate understanding. All were multiple choice, and they represented different interaction types we envisioned for the CodeScribe: comment selection, code comparison, and function ordering.
You can find a copy of the Google Form survey here: You can find a copy of the code survey here:
goo.gl/forms/YdcLBmwADQepxLU42
Results
Ultimately, I was terrifically pleased with the results. After three weeks, the survey had accumulated 55 responses from people distributed across all the defined learning methods and experience levels. The findings helped to confirm that those with absolutely no code education could extract some meaning from the code. CodeScribe certainly is not intended to throw people into plain code without guidance, so to me this was a big assurance that I’m headed in the right direction. From the survey, I also learned a lot about lesson design. I look forward to sharing the next CodeScribe prototype with you soon!
See the results and findings from this hack:
goo.gl/9f3gUA
You can find a copy of the code survey here:
goo.gl/forms/YdcLBmwADQepxLU42
See the data for yourself:
goo.gl/JDvZe7
