Transphobia is being signed into law in the UK. Let's fight it together.
I am begging the UK trans community and its allies to attend the rescheduled Mass Lobby at Parliament, now on Wed Sept 9th, 1pm-5pm, organised by Trans Solidarity Alliance.
The law around trans equality is fundamentally broken. Government inaction is hanging trans people and organisations out to dry - with a con
The new EHRC Code of Practice pushes trans people out of toilets, hospital wards, and community spaces. It normalises gender policing based on appearance and stereotypes. It became statutory guidance in the UK on August 5th, 2026.
Trans people are now legally their assigned gender at birth and must join gendered spaces accordingly, but if they are perceived as their lived gender, they can also be ejected from those spaces. The guidance says: either break the law, or don’t pass too well.
A mass lobby is where you invite your MP to discuss your concerns with you in-person. Our demands have changed. Ask your MP to:
Write to Ministers and Prime Minister Andy Burnham to say they are profoundly concerned by the rolling back of trans rights, and that they believe that it is time for Parliament to legislate to protect equality for trans people.
Ask parliamentary questions focused on protecting trans people’s rights and defending trans people’s abilities to live safe and dignified lives.
Speak up both inside and outside Parliament in defence of trans people’s rights.
Meet regularly with trans constituents to understand their lived realities.
Your MP does not have to be an ally, they do not have to respond to your email for you to show up and greencard them (details below the cut.) What matters is that as many people as possible show up.
I cannot stress this enough: Showing up in person matters. It is much more effective than petitions, emails, and letters.
We have already had some wins because we took action. The 2026 EHRC guidance has already rolled back its most harmful elements because of mass campaigning by individuals like us, and groups like The Good Law Project. This summer, 166 MPs signed the Early Day Motion 240 to reject the EHRC Code of Practice, because of our campaigning. That is 1 in 4 MPs.
It is a horrible, stressful time, and I am so sorry if you're trans and live in the UK. But I was at last year's mass lobby and the line for greencarding alone stretched around the back gates. It was a record breaking mass lobby and made us impossible to ignore. Let's do even better this time. Details under the cut:
Worried about what to say?
Bring your personal worries about transphobia being signed into law, and trans friends being excluded from public spaces. You are a living person who deserves dignity. Remind your MP of that. On the day, you'll get guidance and brochures from Trans Solidarity Alliance that outlines our demands. This is mine from last year.
Here is an online briefing for lobbyists (10 mins read for essential info, 20 mins for details)
Money issues?
Trans Solidarity Alliance provides a travel bursary that you can sign up for via the link.
Got a refusal or no response from your MP?
Come anyway! You can request a same-day appointment with your MP through a process called greencarding. They will come and see you if they’re already in Parliament. Even if they don’t, they’re made acutely aware of your cause because you showed up in person. This is my greencard from last year.
Here is the EHRC Code of Practice in full. It's a tough read, but some highlights are:
Organisations can’t provide trans-inclusive, single-sex services, or they risk being sued for discrimination. e.g. domestic violence support for women including trans women, men’s rugby group including trans men (12.68).
Trans people will have nowhere safe to pee. If you’re a trans man, businesses can't allow you to pee in the men's, and you can also be ejected from women’s bathrooms if you’re perceived as a man. Vice versa for trans women. EHRC suggests a ‘third space’ bathroom, which is discriminatory and unworkable for most businesses. (13.130-133)
Sports organisations must exclude trans people from single-sex competitions (13.73). A women’s only sports competition must exclude trans women because of their biological advantage or face potential lawsuits (13.74), but a trans man who has undergone testosterone treatment can also be excluded based on fairness rules (13.81).
Trans women are stripped of the legal definition of ‘lesbian’, and therefore no longer have legal protections if they’re discriminated against on the basis of sexual orientation. (2.50, 2.92).
Here is the Good Law Project's better explanation of the EHRC Code.
Sign up for the mass lobby and email your MP here, or click the main link above.
If you are not in the UK but want to support the case, please donate to The Good Law Project.
Save the date and book off work!! I will see you there :))
I wake already behind. Not late, not by the clock, but by the invisible schedule everyone else seems born knowing.
The dishes have become geography. Laundry forms mountain ranges. Unread messages bloom like mold in forgotten corners.
A simple task sits on the table for weeks, small as a spoon, heavy as a cathedral. There is a particular exhaustion that comes from translating yourself into a functioning person every morning.
Find keys. Answer email. Make food. Return call. Four pebbles in somebody else's hand. Four planets in mine.
My mind is crowded. Every emotion arrives with all its luggage. Grief drags old photographs. Fear brings spreadsheets. Shame shows up early and refuses to leave. Even joy enters carefully, looking around as though it accidentally knocked on the wrong door.
People ask, How are you? And I stand before the question like a traveler at customs, wondering how to declare the storm system, the static, the fog, the strange machinery that keeps jamming inside my chest.
Some days, existing feels less like living and more like balancing a stack of fragile plates while walking through a hurricane, while everyone else insists the weather is lovely.
I know the responsibilities. They sit beside my bed every night, patient and legitimate.
Bills. Deadlines. Promises. Appointments. They are not villains. But neither are they light. And I am tired of treating every collapse as a moral failure.
And there is the irritation. The endless, shameful irritation. Not cruelty. Not anger, exactly. Just nerves rubbed raw by existing too long without relief. I startle at everything. A notification. A question. The sound of someone calling my name from another room.
My body reacts before I do, like a dog whose ears never stop listening, whose muscles never quite unclench. Always scanning. Always waiting for a threat, a demand, an emergency, even when none arrives.
People see the bark. The sharp answer. The flinch. The impatience. They do not see the exhausting hours spent holding it back. The effort of sitting still while my mind tears around the room. The teeth marks left on every thought I don't allow myself to say.
I am so tired. It lives inside the wiring. Inside the bones. Inside the terrible distance between knowing what needs doing and being able to do it. Every responsibility becomes another object I have to carry while pretending my arms aren't shaking.
And all the while, there is weather inside me. Pressure systems. Thunderheads. Winds strong enough to uproot trees.
People speak of emotional regulation as if I am holding a glass of water. As if I merely need a steadier hand. But what I am trying to hold is a storm inside a plastic bag. The walls stretch. The seams scream. Every day feels like one more impossible attempt to keep lightning contained inside something never built for it.
I am tired of being praised for keeping it together. Together is a strange word for this arrangement. I carry fear. Responsibility. Grief. Noise. Exhaustion. All the unfinished things that follow me from room to room. I carry a nervous system that mistakes survival for living.
And when my voice sharpens, when tears arrive too quickly, when I disappear for a while, when another plate slips from my hands, people see the rupture. They do not see the hours spent holding back weather. They do not see how much effort it takes to appear ordinary.
How many alarms I silence inside myself before breakfast. How much of my life is spent bracing. For disappointment. For demands. For the next thing. I am not falling apart. That would imply there was ever a moment when all of me fit neatly together. No. This is simply the shape my life has taken. Carrying too much. Feeling too much. Waking each morning already exhausted by the fact of having to be awake.
And still, somehow, dragging myself forward. Not because I am brave. Not because I am hopeful. But because morning keeps arriving whether I am ready for it or not.
Industry says government’s failure to address cooling demands means AI growth plans are ‘fatally flawed’
“There appears to be an assumption that the country will always have enough water for its economic needs,” the briefing said. “Nothing could be further from the truth.”
In May, a House of Lords report said England faced a 5bn litre a day shortfall for public water supplies by 2055.
Water UK said datacentres were already being built in water-stressed areas, with more than three-quarters located in the south and east of England. Millions of people in these regions have been placed under a hosepipe ban by water companies after prolonged extreme heat and lack of rainfall.
[...]
The government has designated datacentres as critical national infrastructure, on the basis that their loss or compromise could result in “major detrimental impact on the availability, integrity or delivery of essential services” and “significant impact on national security, national defence, or the functioning of the state”.
Oliver Hayes, head of big tech and campaigns at the campaign group Global Action Plan, said it was not clear why datacentres had been given this status, but that water companies may be powerless to restrict their water use as a result. This could risk datacentres being prioritised over households at times of peak water demand.
“I think it is highly likely that we’re going to end up seeing datacentres that are actually running chatbots, or enabling people to do benign and very wasteful, pointless things, protected and given favourable treatment when it comes to resources,” Hayes said.
So Rikard & I came back yesterday from the International Discworld convention where we both had the most wonderful time... we met wonderful people, saw amazing cosplays, played games, attended fascinating talks.
As members of one of the "Schools of Art" competing over the four days of the Con, we were part of a team putting up an impromptu Commedia dell'arte performance involving a wizard, a lady of the night seamstress and a persistent thief. Rikard was the wizard, trying to summon a dragon by doing his best Swedish Chef mumbling. I was part of the wall behind him (holding a piece of wallpaper appropriately decorated by graffiti). Between that, some charades and the treasure hunt where we all tried to find various props from the Discworld books, our team - presenting, among other things, Cohen's loincloth, Foul Ole Ron's Smell (aka an expired duck egg) and the team members' actual teeth - our team actually won the prize!
Rikard and I also joined the Unseen University Challenge, a Discworld trivia contest; our team lost in the first round, but when the finals came, one of the teams (appropriately named We're All Individuals) found itself missing 3/4 of its members so I snuck back in and we won!
I had bought a little plushie dragon to complete my Sybil portrait, but it kept escaping my pocket to go on its own adventures. It was twice brought back to me by a little girl who had found it, so I let her keep it, which she was very happy about, warming my heart! And she later got it signed by several distinguished guests including Rhianna Pratchett herself.
And of course I spent a lot of time sketching people at queues and events, but this time I tried a new approach: I actually asked people and even offered free commissions! It went awesomely.
Best event ever; nowhere else is even close to similar. For the next one I'm contemplating the idea of submitting a panel of my own...
Really love how the person selling this is wearing black latex fetish gloves to protect the pristine condition of Inch the Worm... Beanie Bondage... BeanieSM...
Okay, so apparently other people associate black gloves with normal jobs and I accidentally hosted my own impromptu Pervert Reveal Party in front of god and everybody via Beanie Baby post. Awesome.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
One of my favorite rhetorical and analytical moves is joining things together (showing that two different, seemingly unrelated ideas are aspects of the same phenomenon) and taking them apart (resolving a paradox by demonstrating that what appears to be one, contradictory thing is actually two different things that have been lumped together).
"Taking things apart" is a very useful framework for understanding AI. How do we resolve the (seeming) paradox that some skilled workers report wonderful results from their work with AI, while others are full of dire warnings about the lurking defects in their AI-assisted outputs? Simple: the first group are "centaurs" (humans who are assisted by machines) and the second are "reverse centaurs" (humans who have been pressed into service as peripherals for machines):
What are we to make of the people who've been fired by bosses who replaced them with AI, in light of the fact that AI is demonstrably not able to do their (former) jobs? Again, it's simple if you separate out two distinct phenomena: "AI can do your job" is the first. The second is: "Your boss is a credulous dolt who is infinitely horny for replacing lippy workers with pliable machines, which made him an easy mark for an AI salesman who convinced him to fire you and replace you with an AI that can't do your job":
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you. They don't have to believe in AI in order to think it's a good investment: like an investor betting that Joe Rogan can sell millions of dollars' worth of peptides to desperate young men, they are assessing the sales potential, not the merits of the thing for sale:
https://pluralistic.net/2026/08/03/andor/#either
As useful as "taking things apart" is, "putting things together" is also a very important technique for assessing, critiquing and improving AI. In a stellar essay entitled "Temperature Zero for Culture: Why Everything Is Starting to Look the Same" by the data scientist Lauren Leek, we get a top-notch example of "putting things together":
Leek's essay is one of those fabulous, wide-ranging, cross-disciplinary pieces, touching on urban design, music trends, synthetic LLM crowds, Netflix recommendation algorithms, and several other subjects, all seeking to resolve a(nother) (seeming) paradox: how is it that we have so much potential variety, but everything is so manifestly the same?
The answer is complicated and nuanced, but Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want."
This is a pervasive issue across many domains. Leek says that economists call it "performativity," while machine learning researchers call it "model collapse" and urbanists call it "placelessness."
"Performativity" describes how, once a market has been modeled by economists, that model becomes the foundation for economic policy, which pushes the market to conform to the model:
"Model collapse" describes how machine learning models that are trained on their own predictions become incredibly bland, with all variety disappearing from the system's predictions:
This is hugely consequential: it's why bias proliferates through predictive policing algorithms: train a model with data from racist stop-and-frisks and it will predict that all the weapons and drugs in a city are to be found in Black and brown peoples' pockets. Turn those predictions into recommendations telling cops where to go look for weapons and drugs and they will double down on racist stops, producing even more biased training data, which turns into still more bias in the predictions:
"Placelessness" is the urbanist's name for "when everywhere optimises toward the same template." I think of it as Flinstones Syndrome, where the same background is looped behind Fred and Barney as they drive through Bedrock. In New York City, it's Citibank-bodega-Chipotle-Walgreens; in the Chicago suburbs, it's the strip malls with a Chili's, a gas station, and a big box store.
Leek proposes that these are all expressions of the same underlying phenomenon, a failure mode of data science that takes a world of "granular personal data" and arrives at a world where "personalisation produc[es] more sameness."
To these excellent examples, I'd add another one, from the world of monetary policy: Goodhart's Law, which holds that "When a measure becomes a target, it ceases to be a good measure":
https://en.wikipedia.org/wiki/Goodhart%27s_law
Goodhart's Law captures a wide variety of phenomena. When Google first deployed Pagerank, they showed that by counting the inbound links to all the pages on the web, you could extract a signal about which pages were most important (because there was no reason to link to a page unless you found it noteworthy).
But once Pagerank became the dominant means by which web users found pages, counting links stopped being useful: first, because people used Pagerank to find the best pages and link to them, making it impossible for new pages to get the inbound links needed to supersede incumbent pages; and second, because it's easy for fraudsters to create inbound links for low-quality pages in bulk, once there's a reason to do so.
Counting inbound links was a world-beating retrospective way of predicting which page would best match a searcher's query, but once it shaped the world it sought to analyze, it ceased to be a good prospective way to predict which page would best match your queries.
Leek is a brilliant data scientist and an even better science communicator, with a knack for crisp, readily understood explanations. How can a world of granular, highly varied data turn into a world of homogeneous choices? Simple: start with a set of items ("cuisines, genres, shop types") and a standard algorithm for sorting them. Let users choose from those recommendations. The mode (average) of those choices "gets shown more, so it gets picked more, so the model grows more confident the mode is what people want, and the tails starve." Run this for a few rounds and the evenly distributed catalog of choices "collapses onto one dominant option."
This is intrinsic in the choices we make in designing recommendation algorithms, tilting them towards the likelihood of a successful recommendation. A recommender that wants to succeed every time will make the safest possible recommendations, "so an algorithm that is uncertain about you, and it is always at least a little uncertain, hedges toward the average."
Then she busts out a beautiful, perfect little statistics aphorism: "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
This is how recommendation systems – and other analytical tools that produce predictions that are then turned into action – force so many diverse phenomena (streets, markets, media recommendations) into sameness. The fact that these recommenders are self-fulfilling prophecies means that "they don't have to be right," only "listened to."
This explains the sameness of so many of London's high streets. Leek examines 640 shopping streets, characterizing 18,000 food places spread out across them, flagging all the chain restaurants. Her analysis shows that any two London streets will, on average, share about half of their "food profile."
Obviously, this is most pronounced on streets with chain outlets, and it doesn't take that many chain outlets before a street's sameness shoots up: "A relatively small number of repeated names is enough to make otherwise different streets resemble one another more." So why do streets with chains resemble one another so much? Because the chains use an algorithm (weighting footfall, proximity to train stations, demographics, and competitors) to decide where to put their restaurants. If a street with a Gail's Bakery on it feels like every other street with a Gail's Bakery, that's because Gail's only puts its restaurants in places that have highly similar characteristics, measured to a high degree of accuracy and controlled by a narrow set of tolerances.
In other words, every street that feels like it should have a Gail's will eventually get a Gail's, whereupon that street will feel even more like all the other streets that have a Gail's, because it will share one more common factor with those other streets (a Gail's).
Leek points here to her earlier work on pub closures in the UK. The UK has experienced an epidemic of pub closures, with thousands of pubs disappearing since 2016:
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This same phenomenon acts on culture. In an age of music recommendation algorithms, hit songs are changing; today's songs use a smaller vocabulary of unique words and repeat those words more often:
Vocabulary richness, distinct words relative to length, has fallen by more than a quarter since the early 1960s, while the share of repeated lines has climbed by nearly a third. The modern hit says less and says it more often, because the hook that works gets repeated.
But that's not the whole story! While each song resembles itself more ("saying less more often"), within that constraint, there's far more variety today than before: a given song's (constrained) vocabulary has grown more distinct when compared to all the other songs' vocabularies. Songs repeat the words they use, but the words repeated in songs are getting more different.
For Leek, this is the key to understanding the whole phenomenon and (more importantly) doing something about it. Music recommendation systems optimized for a singable hook, but did not optimize on any of the other variables in songs, so those dimensions acquired a broader range, even as the optmized variable got flatter and narrower.
This means that the tendency of recommenders to "flatten the world" isn't a single blunt outcome: it depends on which dimension we choose to flatten through recommendation, and who chooses to flatten that dimension.
A media recommender optimizes for consumption, showing you a tractable set of things it believes you'll watch, read or listen to. When you choose from among this limited set, the recommender takes note of that fact and shows you more of the same, pushing everything to a greige median. All the movies, books and songs you might have liked that were omitted from that initial set are excluded from being recommended in the future. The features of that media that you might have appreciated "decay out of consideration." They are never tested for desirability. The model collapses.
How badly does it collapse? Leek cites Movietweetings' data on which movies people watch: out of a million public movie ratings, half relate to the top 2% of movies in the set. There's 38,000 films in the set, but just 380 titles account for 40% of the ratings. Leek argues (persuasively) that this isn't because recommenders are good at "knowing your taste" – rather, they are good at "narrowing the menu."
Leek relates this to her work on creating LLM "personas" – synthetic populations meant to mimic the tastes and proclivities of real groups of people, that you can interrogate "before you spend money asking actual humans." While this would be useful for many applications, "it fails in exactly the way this whole essay is about."
Leek went to enormous lengths to reproduce the traits that make people interesting to study in aggregate, painstakingly replicating the ways that social connections, psychological outlook and demographic factors predict people's beliefs. The result was a set of LLM personas with "elaborate stories" about how they differed from one another, but whose survey responses about planned actions were homogeneous in a way that real populations are not.
This, Leek writes, is the same force that homogenizes other data-driven predictors. Because she'd ordered her LLM to reproduce the statistically validated relationships between different factors that predict a person's beliefs, each synthetic persona was a homogenized average. It's like the paradox of "The Average Man," where military uniforms sized to the average of all service personnel fit no one, because no one is average:
https://archive.org/details/DTIC_AD0010203
The thing is (as Leek points out) the idea that synthetic personas are a good way to understand the preferences of a real population is not a harmless delusion: it's a product that's being actively sold to governments, campaigning politicians and marketers. It's a self-fulfilling prophecy that drives governance, political campaigns and product design to the same homogeneous median that is making every shopping street in London feel the same.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms. As anyone whose favorite banana went extinct can tell you, homogeneity works well, but diversity fails well:
https://en.wikipedia.org/wiki/Gros_Michel
The brittleness of algorithm-induced homogeneity is compounded by the fact that recommenders obscure the true preferences of people. If you watch two Scandinavian crime dramas after Netflix recommends them to you, it will keep showing you more Scandy crime for the next decade – even if there's another kind of programming that you'd vastly prefer (if only you knew about it). This means that decision-makers who choose which shows will get made in the future will keep on funding their safe Danish detectives, to the exclusion of whatever might emerge from the same weird attractor that produced the K-Pop Demon Hunter fortune.
Transpose this failure mode onto states, bank managers and landlords, and we see whole ranges of policies, businesses and activities that never come into existence, despite the popularity, prosperity and joy they might bring us.
But Leek doesn't end with this worrisome note. Instead, she identifies this whole thing – model collapse, placelessness, performativity, even Goodhart's Law – as an expression of one of the best-understood tradeoffs in computer science: "exploration vs exploitation":
Any system learning from feedback has to divide its effort between exploiting what already scores well and exploring options it hasn’t tried, in case they’re better.
Computer scientists have long understood that focusing on exploitation to the exclusion of exploration is a trap that locks you into "the first decent option" so you can never discover the best one.
Which means that this algorithmic homogeneity has a well-understood corrective: "forcing exploration back in." The problem is that markets hate this kind of exploration. A company that lives and dies by how many clicks it gets is never going to sacrifice 20% of its traffic by showing its users weird, untested options that score worse than the median because these weird things have never had a chance to prove that they are desirable.
This is a classic market failure, and, as Leek points out, there are regulatory responses in the UK (the Digital Markets, Competition and Consumers Act) and the EU (the Digital Services Act), both of which require the largest platforms to open up their recommendation systems, but so far, regulators have focused on "online harms" rather than variety (though the DSA does require platforms to offer algorithmic recommendations that are not based on your personal traits).
Leek identifies this willingness of states to set conditions for algorithm design as a means by which "exploration" can be forced back into the system. She's also bullish on interoperability, so that users can leave platforms with bad recommenders, without losing access to their media or social circles. As she writes, "the deepest discipline on a feed that has trapped you is the credible ability to leave it and take your data with you." I couldn't agree more:
She's less hopeful about individual responses. Demanding that you be an "adventurous consumer" is a way of letting systems off the hook. When every street has the same restaurants and every bookshop has the same books and the people in your life are all locked into one of two social media platforms, "choosing wisely" only gets you so far. Shopping isn't politics!
Leek is a superb writer. After reading this piece yesterday, I sent it to half a dozen people and then read everything else in Leek's newsletter archives. Not only is it all brilliant, but I also realized that she'd written one of the most memorable articles about cities and platforms I've read in the last year, "How Google Maps quietly allocates survival across London’s restaurants – and how I built a dashboard to see through it":
I should have added Leek's newsletter to my RSS reader when I read that last December. I've rectified that oversight! What a fantastic thinker, scientist and communicator! If she isn't being relentlessly pestered by editors and literary agents offering her a book deal, then it really does prove that the recommender systems are elevating the bland median over the thoroughly, delightfully spiky outliers.
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