Hypothetical AI election disinformation risks vs real AI harms
And those real AI harms are priming people to believe disinformation.
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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://pluralistic.net/wp-admin/post.php?post=8119&action=edithttps://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:
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:
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
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Cryteria (modified)
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