#self learning algorithms

[procurement-insights]

Here is the third of today's three related posts asking; what does clean data and dirty laundry have in common?

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

 — 

Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

[hussyknee]

I'm really not a villain enjoyer. I love anti-heroes and anti-villains. But I can't see fictional evil separate from real evil. As in not that enjoying dark fiction means you condone it, but that all fiction holds up some kind of mirror to the world as it is. Killing innocent people doesn't make you an iconic lesbian girlboss it just makes you part of the mundane and stultifying black rot of the universe.

"But characters struggling with honour and goodness and the egoism of being good are so boring." Cool well some of us actually struggle with that stuff on the daily because being a good person is complicated and harder than being an edgelord.

Sure you can use fiction to explore the darkness of human nature and learn empathy, but the world doesn't actually suffer from a deficit of empathy for powerful and privileged people who do heinous stuff. You could literally kill a thousand babies in broad daylight and they'll find a way to blame your childhood trauma for it as long as you're white, cisgender, abled and attractive, and you'll be their poor little meow meow by the end of the week. Don't act like you're advocating for Quasimodo when you're just making Elon Musk hot, smart and gay.

[queen-mabs-revenge]

communist generative ai boosters on this website truly like

[thebowlercapfairy]

I search things because I'm stupid and I don't know about them and I want to know

You search things because you think you know about them, but you want validation from search engine daddy that you do

We are not the same

Except for the fact that we're both stupid

[oblako]

fell down the youtube rabbithole of essays about the indie internet and 90/y2k-style personal and self-made websites on neocities et al as counterculture to the modern internet and guess what ads i keep getting. fuckin. ai-powered website tools where you write a prompt and get a ready-to-use pre-made website in minutes. brb i'm gonna go kill all ai and marketing companies with hammers 💥💥💥

[dead-generations]

I do wonder how Peter Watts would react to knowing that many people, including myself, read Blindsight and had no trouble imaging they are Siri Keeton. because he is literally me

[possqueen]

today im getting really mad about people using AI to describe literally anything and using it as a means to get mad at anything and everything described by it without looking into any context. i see people get mad about "ougghghhuhg furiosa composer used AI" and then i read into the article and it's just "yeah we used a voice synth and vocoder to make a really weird funky sound"

[emperornero]

just finished watching brett leonard's 'virtuosity' (1995) [zabójcza perfekcja in polish. whats up with his movie titles having incredible polish translations . hello]. probably his most competent movie [from the ones i have seen] i had a lot of fun with it . sid is so fucking funny he belongs in a cartoon. close him up in a terrarium watch his behavior under a microscope

probably the best and the funniest movie directed by leonard. idk if the second thing here is good considering this was supposed to be an action sci-fi that in some moments tries to tackle some heavy topics but oh well . sid's one-liners and his character did it for me. would be my favourite movie from the guy but im a masochist and theres already a different one taking up this spot

[procurement-insights]

Why is achieving clean data impossible without a sound learning loopback process for self-learning algorithms?

[toilunderthemoon]

if i start to read a post but notice that it has like 10k + notes, im always at least slightly more skeptical about how funny/true/logically sound it is

[compassionmattersmost]

The Gentle Path: A Bedtime Story for Children with ME/CFS

In a small village nestled between quiet hills and flowing rivers, there lived a kind, gentle teacher named Sage. Everyone in the village loved Sage because he carried a peaceful presence, like a calm breeze on a warm day. People often came to him for guidance, and he always had time to sit with them, no matter how small or big their worries were. One day, a group of children gathered by the…

[raffaellopalandri]

Curating Purpose: Hobbies as Expressions of Mindfulness and Impermanence

Daily writing promptWhat is your favorite hobby or pastime?View all responses When it comes to discussing my hobbies, it’s essential to first address the concept of “favourites.” I do not have a favourite of anything, as favouring one thing over another would imply attachment, and attachment can lead to a sense of permanence or possessiveness—an idea I do not resonate with. Photo by Kevin…

[fortunately-bi]

Do the followers know I'm a metal head? Do they know that I want to be covered in tattoos with an alternative look who listens to obscure metal bands and stuff? Or do I still come off as like, the nerd in the basement lmao

[haru-dipthong]

I had an argument several months ago in a japanese learning discord server that still annoys me whenever I think about it. It annoys me because I lost the argument so I’ve written a dramatic reenactment with Northernlion cast as myself and NL’s chat as The Rest Of The Discord Server in which I win the argument to soothe the psychic wound this shit has wrought on me.

NL: [while in the balatro shop] I dunno man, call me crazy but I think sometimes it would be better for some people to learn casual form first. I dunno why the masu-form-first thing is so universal. [reading chat] “-2, -2, it's just how everyone does it” Oh so just because everyone does it it means it has to be universally applied in every classroom and by every self-taught language learner without exception? C’mon dude, the world is a complicated place, let's not be boiling things down into absolutes. [opens arcana pack and puts finger on chin] What am I doing here? [gets distracted by chat again] “It’s probably more efficient for learning that’s why everyone does it.”? How do you figure that? Also literally every single native japanese speaker learns casual form first bro, they’re speaking with family. [voice starts getting more bombastic] And I can tell you now, an infant is learning quicker than you. You’ve probably been studying for four years on a 12 hundred day duolingo streak trying to minmax your spaced repetition algorithm on your fuckin uhh anki deck, you got the JLPT N3 kanji list memorised and still can’t say the three words you need in an average exchange with the lawson clerk - meanwhile a toddler whose brain isn’t even half as developed as yours is spitting coherent grammatical japanese 12 straight hours a day, and you think your method is [does finger air quotes] “more efficient”. Right. [pauses for 2 seconds] Now I know what you’re gonna say: [whiny nerd voice] “oough but NL first language and second language acquisition are different” [starting to sound genuinely pissed] Oh yeah how? How are they different and how does that difference mean that this specific thing needs to be taught differently? Read some Krashen bro, educate yourself. [picks the hermit, goes to next stake] [makes another inadvisable glance at chat] [reading in a doubtful tone] "In a classroom setting you have to speak formally to a teacher, it’s a respect thing.”? Some people don’t learn from a classroom, man! Some people learn from their families and friends and partners! A lotta people are self taught, are you telling me they need to start genuflecting to themselves?! ALL I’m sayin is that SOME people [puts both hands up to camera] NOT ALL, SOME people MIGHT have a better learning experience if they started with casual form, okay?