#machine bias

[olivergisttv]

Human vs Machine: The Ethics of AI Co-Workers in the 2025 Workplace

As AI tools become office regulars, from virtual assistants to decision-making bots, the 2025 workplace is no longer just human — it’s a hybrid of code and consciousness. But with this shift comes a critical question: Can we trust our AI co-workers? Human vs Machine: The Ethics of AI Co-Workers in the 2025 Workplace The Rise of Machine Colleagues From drafting emails to summarizing meetings, AI…

[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.

[mcytegg]

veni's deeply biased tierlist on how evil it is to kill each LS member LOL

[sovonight]

,

[waitingforsecretsouls]

anyway that contrast between #630 and #640 sure was crazy, huh.

[chai-en-kaadhale]

i was looking for my notes LMAO

i dont even have ultrakill(yet)

[ixnai]

The allure of speed in technology development is a siren’s call that has led many innovators astray. “Move fast and break things” is a mantra that has driven the tech industry for years, but when applied to artificial intelligence, it becomes a perilous gamble. The rapid iteration and deployment of AI systems without thorough vetting can lead to catastrophic consequences, akin to releasing a flawed algorithm into the wild without a safety net.

AI systems, by their very nature, are complex and opaque. They operate on layers of neural networks that mimic the human brain’s synaptic connections, yet they lack the innate understanding and ethical reasoning that guide human decision-making. The haste to deploy AI without comprehensive testing is akin to launching a spacecraft without ensuring the integrity of its navigation systems. The potential for error is not just probable; it is inevitable.

The pitfalls of AI are numerous and multifaceted. Bias in training data can lead to discriminatory outcomes, while lack of transparency in decision-making processes can result in unaccountable systems. These issues are compounded by the “black box” nature of many AI models, where even the developers cannot fully explain how inputs are transformed into outputs. This opacity is not merely a technical challenge but an ethical one, as it obscures accountability and undermines trust.

To avoid these pitfalls, a paradigm shift is necessary. The development of AI must prioritize robustness over speed, with a focus on rigorous testing and validation. This involves not only technical assessments but also ethical evaluations, ensuring that AI systems align with societal values and norms. Techniques such as adversarial testing, where AI models are subjected to challenging scenarios to identify weaknesses, are crucial. Additionally, the implementation of explainable AI (XAI) can demystify the decision-making processes, providing clarity and accountability.

Moreover, interdisciplinary collaboration is essential. AI development should not be confined to the realm of computer scientists and engineers. Ethicists, sociologists, and legal experts must be integral to the process, providing diverse perspectives that can foresee and mitigate potential harms. This collaborative approach ensures that AI systems are not only technically sound but also socially responsible.

In conclusion, the reckless pursuit of speed in AI development is a dangerous path that risks unleashing untested and potentially harmful technologies. By prioritizing thorough testing, ethical considerations, and interdisciplinary collaboration, we can harness the power of AI responsibly. The future of AI should not be about moving fast and breaking things, but about moving thoughtfully and building trust.

[lichqueenlibrarian]

Kirk went to all that effort not to mention that one of the missing agents, Agent du Molin is “a very good friend” only for McCoy to immediately tell Spock, who has NEVER heard of this woman (whoops).

I’m sure Spock declaring to go to the planet in Kirk’s place is totally altruistic.

[pettyprocrastination]

Bought a booksleeve at barnes and nobles this weekend for my freewrite- they only had lile two designs and the size was BARELY enough to fit it so I had to fight with the zipper to get it in. Got into the parking lot and thought "I could've probably just made one" and decided that would be the incentive to finally put my sewing machine to use in 2024

[mostlysignssomeportents]

Hypothetical AI election disinformation risks vs real AI harms

I'm on tour with my new novel The Bezzle! Catch me TONIGHT (Feb 27) in Portland at Powell's. Then, onto Phoenix (Changing Hands, Feb 29), Tucson (Mar 9-12), and more!

You can barely turn around these days without encountering a think-piece warning of the impending risk of AI disinformation in the coming elections. But a recent episode of This Machine Kills podcast reminds us that these are hypothetical risks, and there is no shortage of real AI harms:

https://soundcloud.com/thismachinekillspod/311-selling-pickaxes-for-the-ai-gold-rush

The algorithmic decision-making systems that increasingly run the back-ends to our lives are really, truly very bad at doing their jobs, and worse, these systems constitute a form of "empiricism-washing": if the computer says it's true, it must be true. There's no such thing as racist math, you SJW snowflake!

https://slate.com/news-and-politics/2019/02/aoc-algorithms-racist-bias.html

Nearly 1,000 British postmasters were wrongly convicted of fraud by Horizon, the faulty AI fraud-hunting system that Fujitsu provided to the Royal Mail. They had their lives ruined by this faulty AI, many went to prison, and at least four of the AI's victims killed themselves:

https://en.wikipedia.org/wiki/British_Post_Office_scandal

Tenants across America have seen their rents skyrocket thanks to Realpage's landlord price-fixing algorithm, which deployed the time-honored defense: "It's not a crime if we commit it with an app":

https://www.propublica.org/article/doj-backs-tenants-price-fixing-case-big-landlords-real-estate-tech

Housing, you'll recall, is pretty foundational in the human hierarchy of needs. Losing your home – or being forced to choose between paying rent or buying groceries or gas for your car or clothes for your kid – is a non-hypothetical, widespread, urgent problem that can be traced straight to AI.

Then there's predictive policing: cities across America and the world have bought systems that purport to tell the cops where to look for crime. Of course, these systems are trained on policing data from forces that are seeking to correct racial bias in their practices by using an algorithm to create "fairness." You feed this algorithm a data-set of where the police had detected crime in previous years, and it predicts where you'll find crime in the years to come.

But you only find crime where you look for it. If the cops only ever stop-and-frisk Black and brown kids, or pull over Black and brown drivers, then every knife, baggie or gun they find in someone's trunk or pockets will be found in a Black or brown person's trunk or pocket. A predictive policing algorithm will naively ingest this data and confidently assert that future crimes can be foiled by looking for more Black and brown people and searching them and pulling them over.

Obviously, this is bad for Black and brown people in low-income neighborhoods, whose baseline risk of an encounter with a cop turning violent or even lethal. But it's also bad for affluent people in affluent neighborhoods – because they are underpoliced as a result of these algorithmic biases. For example, domestic abuse that occurs in full detached single-family homes is systematically underrepresented in crime data, because the majority of domestic abuse calls originate with neighbors who can hear the abuse take place through a shared wall.

But the majority of algorithmic harms are inflicted on poor, racialized and/or working class people. Even if you escape a predictive policing algorithm, a facial recognition algorithm may wrongly accuse you of a crime, and even if you were far away from the site of the crime, the cops will still arrest you, because computers don't lie:

https://www.cbsnews.com/sacramento/news/texas-macys-sunglass-hut-facial-recognition-software-wrongful-arrest-sacramento-alibi/

Trying to get a low-waged service job? Be prepared for endless, nonsensical AI "personality tests" that make Scientology look like NASA:

https://futurism.com/mandatory-ai-hiring-tests

Service workers' schedules are at the mercy of shift-allocation algorithms that assign them hours that ensure that they fall just short of qualifying for health and other benefits. These algorithms push workers into "clopening" – where you close the store after midnight and then open it again the next morning before 5AM. And if you try to unionize, another algorithm – that spies on you and your fellow workers' social media activity – targets you for reprisals and your store for closure.

If you're driving an Amazon delivery van, algorithm watches your eyeballs and tells your boss that you're a bad driver if it doesn't like what it sees. If you're working in an Amazon warehouse, an algorithm decides if you've taken too many pee-breaks and automatically dings you:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

If this disgusts you and you're hoping to use your ballot to elect lawmakers who will take up your cause, an algorithm stands in your way again. "AI" tools for purging voter rolls are especially harmful to racialized people – for example, they assume that two "Juan Gomez"es with a shared birthday in two different states must be the same person and remove one or both from the voter rolls:

https://www.cbsnews.com/news/eligible-voters-swept-up-conservative-activists-purge-voter-rolls/

Hoping to get a solid education, the sort that will keep you out of AI-supervised, precarious, low-waged work? Sorry, kiddo: the ed-tech system is riddled with algorithms. There's the grifty "remote invigilation" industry that watches you take tests via webcam and accuses you of cheating if your facial expressions fail its high-tech phrenology standards:

https://pluralistic.net/2022/02/16/unauthorized-paper/#cheating-anticheat

All of these are non-hypothetical, real risks from AI. The AI industry has proven itself incredibly adept at deflecting interest from real harms to hypothetical ones, like the "risk" that the spicy autocomplete will become conscious and take over the world in order to convert us all to paperclips:

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

Whenever you hear AI bosses talking about how seriously they're taking a hypothetical risk, that's the moment when you should check in on whether they're doing anything about all these longstanding, real risks. And even as AI bosses promise to fight hypothetical election disinformation, they continue to downplay or ignore the non-hypothetical, here-and-now harms of AI.

There's something unseemly – and even perverse – about worrying so much about AI and election disinformation. It plays into the narrative that kicked off in earnest in 2016, that the reason the electorate votes for manifestly unqualified candidates who run on a platform of bald-faced lies is that they are gullible and easily led astray.

But there's another explanation: the reason people accept conspiratorial accounts of how our institutions are run is because the institutions that are supposed to be defending us are corrupt and captured by actual conspiracies:

https://memex.craphound.com/2019/09/21/republic-of-lies-the-rise-of-conspiratorial-thinking-and-the-actual-conspiracies-that-fuel-it/

The party line on conspiratorial accounts is that these institutions are good, actually. Think of the rebuttal offered to anti-vaxxers who claimed that pharma giants were run by murderous sociopath billionaires who were in league with their regulators to kill us for a buck: "no, I think you'll find pharma companies are great and superbly regulated":

https://pluralistic.net/2023/09/05/not-that-naomi/#if-the-naomi-be-klein-youre-doing-just-fine

Institutions are profoundly important to a high-tech society. No one is capable of assessing all the life-or-death choices we make every day, from whether to trust the firmware in your car's anti-lock brakes, the alloys used in the structural members of your home, or the food-safety standards for the meal you're about to eat. We must rely on well-regulated experts to make these calls for us, and when the institutions fail us, we are thrown into a state of epistemological chaos. We must make decisions about whether to trust these technological systems, but we can't make informed choices because the one thing we're sure of is that our institutions aren't trustworthy.

Ironically, the long list of AI harms that we live with every day are the most important contributor to disinformation campaigns. It's these harms that provide the evidence for belief in conspiratorial accounts of the world, because each one is proof that the system can't be trusted. The election disinformation discourse focuses on the lies told – and not why those lies are credible.

That's because the subtext of election disinformation concerns is usually that the electorate is credulous, fools waiting to be suckered in. By refusing to contemplate the institutional failures that sit upstream of conspiracism, we can smugly locate the blame with the peddlers of lies and assume the mantle of paternalistic protectors of the easily gulled electorate.

But the group of people who are demonstrably being tricked by AI is the people who buy the horrifically flawed AI-based algorithmic systems and put them into use despite their manifest failures.

As I've written many times, "we're nowhere near a place where bots can steal your job, but we're certainly at the point where your boss can be suckered into firing you and replacing you with a bot that fails at doing your job"

https://pluralistic.net/2024/01/15/passive-income-brainworms/#four-hour-work-week

The most visible victims of AI disinformation are the people who are putting AI in charge of the life-chances of millions of the rest of us. Tackle that AI disinformation and its harms, and we'll make conspiratorial claims about our institutions being corrupt far less credible.

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:

https://pluralistic.net/2024/02/27/ai-conspiracies/#epistemological-collapse

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

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

[frank-olivier]

The Future of Justice: Navigating the Intersection of AI, Judges, and Human Oversight

One of the main benefits of AI in the justice system is its ability to analyze vast amounts of data and identify patterns that human judges may not notice. For example, the use of AI in the U.S. justice system has led to a significant reduction in the number of misjudgments, as AI-powered tools were able to identify potential biases in the data and make more accurate recommendations.

However, the use of AI in the justice system also raises significant concerns about the role of human judges and the need for oversight. As AI takes on an increasingly important role in decision-making, judges must find the balance between trusting AI and exercising their own judgement. This requires a deep understanding of the technology and its limitations, as well as the ability to critically evaluate the recommendations provided by AI.

The European Union's approach to AI in justice provides a valuable framework for other countries to follow. The EU's framework emphasizes the need for human oversight and accountability and recognizes that AI is a tool that should support judges, not replace them. This approach is reflected in the EU's General Data Protection Regulation (GDPR), which requires AI systems to be transparent, explainable and accountable.

The use of AI in the justice system also comes with its pitfalls. One of the biggest concerns is the possibility of bias in AI-generated recommendations. When AI is trained with skewed data, it can perpetuate and even reinforce existing biases, leading to unfair outcomes. For example, a study by the American Civil Liberties Union found that AI-powered facial recognition systems are more likely to misidentify people of color than white people.

To address these concerns, it is essential to develop and implement robust oversight mechanisms to ensure that AI systems are transparent, explainable and accountable. This includes conducting regular audits and testing of AI systems and providing clear guidelines and regulations for the use of AI in the justice system.

In addition to oversight mechanisms, it is also important to develop and implement education and training programs for judges and other justice professionals. This will enable them to understand the capabilities and limitations of AI, as well as the potential risks and challenges associated with its use. By providing judges with the necessary skills and knowledge, we can ensure that AI is used in a way that supports judges and enhances the fairness and accountability of the justice system.

Human Centric AI - Ethics, Regulation. and Safety (Vilnius University Faculty of Law, October 2024)

Friday, November 1, 2024

[stephgingrich]

inhumane conditions to go to the gym today (cold, rainy, bad tummy) but i got a genuine wow reaction when i flexed this weekend and im gonna need that to carry me through the next like three weeks

[orthoceras]

strap of my favorite bra is unraveling :/ time to figure out how to sew it back together w/ stitching that doesn't feel annoying on bare skin. and probably gonna have to hand-sew it bc I do Not trust my ability to machine-sew smth this narrow

[ixnai]

Impute is the silent saboteur in AI systems. It is the process of filling in missing data, a seemingly innocuous task that can lead to catastrophic misjudgments. In the realm of artificial intelligence, where algorithms are trained on vast datasets, the integrity of input data is paramount. Yet, imputation introduces a layer of abstraction that can distort reality, creating a veneer of completeness that belies the underlying uncertainty.

Consider an AI model designed to predict financial markets. It relies on historical data, but gaps are inevitable. Imputation steps in, employing statistical methods like mean substitution or regression imputation to fill these voids. However, these methods assume a level of homogeneity that rarely exists in complex systems. The imputed values, while mathematically sound, may not reflect the nuanced dynamics of the market. This is where the danger lies.

AI systems, particularly those driven by machine learning, are not inherently equipped to question the validity of their inputs. They operate under the assumption that the data is a faithful representation of reality. When imputed data is treated as gospel, the AI’s predictions can veer into the realm of fantasy. This is especially perilous when the AI is deployed in high-stakes environments, such as autonomous vehicles or healthcare diagnostics, where erroneous predictions can have dire consequences.

Defending against this blind acceptance requires a multifaceted approach. First, transparency in the imputation process is crucial. AI developers must document the methods used and the assumptions made, allowing for scrutiny and validation by domain experts. Second, incorporating uncertainty quantification can provide a measure of confidence in the imputed values, highlighting areas where predictions may be less reliable.

Moreover, adversarial testing can expose the vulnerabilities introduced by imputation. By deliberately introducing perturbations in the data and observing the AI’s response, developers can identify weaknesses and refine the model’s robustness. This proactive stance is essential in ensuring that AI systems remain resilient in the face of incomplete or imperfect data.

Ultimately, the key to defending against AI’s uncompromising nature lies in fostering a culture of skepticism. Developers and stakeholders must remain vigilant, questioning the assumptions that underpin their models and the data they consume. By acknowledging the limitations of imputation and striving for greater transparency and accountability, we can mitigate the risks and harness the true potential of artificial intelligence.

[zeldasadork]

how do the mounders get even better every session. what the heck. if my creative niche were writing instead of art it would be all over for you guys