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Ed Webb

I unintentionally created a biased AI algorithm 25 years ago - tech companies are still... - 0 views

theconversation.com/making-the-same-mistake-203734

AI algorithms race racism bias DickinsonCollege dickinson technology opinion analysis

shared by Ed Webb on 28 May 23 - Cached
  • How and why do well-educated, well-intentioned scientists produce biased AI systems? Sociological theories of privilege provide one useful lens.
  • Their training data is biased. They are designed by an unrepresentative group. They face the mathematical impossibility of treating all categories equally. They must somehow trade accuracy for fairness. And their biases are hiding behind millions of inscrutable numerical parameters.
  • fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent evidence of this grim reality. The commercial necessity of building market share led to the premature release of these systems.
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  • Scientists also face a nasty subconscious dilemma when incorporating diversity into machine learning models: Diverse, inclusive models perform worse than narrow models.
  • biased AI systems can still be created unintentionally and easily. It’s also clear that the bias in these systems can be harmful, hard to detect and even harder to eliminate.
  • with North American computer science doctoral programs graduating only about 23% female, and 3% Black and Latino students, there will continue to be many rooms and many algorithms in which underrepresented groups are not represented at all.
Ed Webb

What we still haven't learned from Gamergate - Vox - 0 views

www.vox.com/...impact-changes-harassment-laws

internet USA US politics trump gamers alt-right fascism misogyny

shared by Ed Webb on 22 Jan 20 - Cached
  • Harassment and misogyny had been problems in the community for years before this; the deep resentment and anger toward women that powered Gamergate percolated for years on internet forums. Robert Evans, a journalist who specializes in extremist communities and the host of the Behind the Bastards podcast, described Gamergate to me as partly organic and partly born out of decades-long campaigns by white supremacists and extremists to recruit heavily from online forums. “Part of why Gamergate happened in the first place was because you had these people online preaching to these groups of disaffected young men,” he said. But what Gamergate had that those previous movements didn’t was an organized strategy, made public, cloaking itself as a political movement with a flimsy philosophical stance, its goals and targets amplified by the power of Twitter and a hashtag.
  • The hate campaign, we would later learn, was the moment when our ability to repress toxic communities and write them off as just “trolls” began to crumble. Gamergate ultimately gave way to something deeper, more violent, and more uncontrollable.
  • Police have to learn how to keep the rest of us safe from internet mobs
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  • the justice system continues to be slow to understand the link between online harassment and real-life violence
  • In order to increase public safety this decade, it is imperative that police — and everyone else — become more familiar with the kinds of communities that engender toxic, militant systems of harassment, and the online and offline spaces where these communities exist. Increasingly, that means understanding social media’s dark corners, and the types of extremism they can foster.
  • Businesses have to learn when online outrage is manufactured
  • There’s a difference between organic outrage that arises because an employee actually does something outrageous, and invented outrage that’s an excuse to harass someone whom a group has already decided to target for unrelated reasons — for instance, because an employee is a feminist. A responsible business would ideally figure out which type of outrage is occurring before it punished a client or employee who was just doing their job.
  • Social media platforms didn’t learn how to shut down disingenuous conversations over ethics and free speech before they started to tear their cultures apart
  • Dedication to free speech over the appearance of bias is especially important within tech culture, where a commitment to protecting free speech is both a banner and an excuse for large corporations to justify their approach to content moderation — or lack thereof.
  • Reddit’s free-speech-friendly moderation stance resulted in the platform tacitly supporting pro-Gamergate subforums like r/KotakuInAction, which became a major contributor to Reddit’s growing alt-right community. Twitter rolled out a litany of moderation tools in the wake of Gamergate, intended to allow harassment targets to perpetually block, mute, and police their own harassers — without actually effectively making the site unwelcome for the harassers themselves. And YouTube and Facebook, with their algorithmic amplification of hateful and extreme content, made no effort to recognize the violence and misogyny behind pro-Gamergate content, or police them accordingly.
  • All of these platforms are wrestling with problems that seem to have grown beyond their control; it’s arguable that if they had reacted more swiftly to slow the growth of the internet’s most toxic and misogynistic communities back when those communities, particularly Gamergate, were still nascent, they could have prevented headaches in the long run — and set an early standard for how to deal with ever-broadening issues of extremist content online.
  • Violence against women is a predictor of other kinds of violence. We need to acknowledge it.
  • Somehow, the idea that all of that sexism and anti-feminist anger could be recruited, harnessed, and channeled into a broader white supremacist movement failed to generate any real alarm, even well into 2016
  • many of the perpetrators of real-world violence are radicalized online first
  • It remains difficult for many to accept the throughline from online abuse to real-world violence against women, much less the fact that violence against women, online and off, is a predictor of other kinds of real-world violence
  • Politicians and the media must take online “ironic” racism and misogyny seriously
  • Gamergate masked its misogyny in a coating of shrill yelling that had most journalists in 2014 writing off the whole incident as “satirical” and immature “trolling,” and very few correctly predicting that Gamergate’s trolling was the future of politics
  • Gamergate was all about disguising a sincere wish for violence and upheaval by dressing it up in hyperbole and irony in order to confuse outsiders and make it all seem less serious.
  • Gamergate simultaneously masqueraded as legitimate concern about ethics that demanded audiences take it seriously, and as total trolling that demanded audiences dismiss it entirely. Both these claims served to obfuscate its real aim — misogyny, and, increasingly, racist white supremacy
  • The public’s failure to understand and accept that the alt-right’s misogyny, racism, and violent rhetoric is serious goes hand in hand with its failure to understand and accept that such rhetoric is identical to that of President Trump
  • deploying offensive behavior behind a guise of mock outrage, irony, trolling, and outright misrepresentation, in order to mask the sincere extremism behind the message.
  • many members of the media, politicians, and members of the public still struggle to accept that Trump’s rhetoric is having violent consequences, despite all evidence to the contrary.
  • The movement’s insistence that it was about one thing (ethics in journalism) when it was about something else (harassing women) provided a case study for how extremists would proceed to drive ideological fissures through the foundations of democracy: by building a toxic campaign of hate beneath a veneer of denial.
Ed Webb

AI Causes Real Harm. Let's Focus on That over the End-of-Humanity Hype - Scientific Ame... - 0 views

www.scientificamerican.com/...ot-imaginary-existential-risks

opinion analysis AI class race computing propaganda science

shared by Ed Webb on 11 Aug 23 - Cached
  • Wrongful arrests, an expanding surveillance dragnet, defamation and deep-fake pornography are all actually existing dangers of so-called “artificial intelligence” tools currently on the market. That, and not the imagined potential to wipe out humanity, is the real threat from artificial intelligence.
  • Beneath the hype from many AI firms, their technology already enables routine discrimination in housing, criminal justice and health care, as well as the spread of hate speech and misinformation in non-English languages. Already, algorithmic management programs subject workers to run-of-the-mill wage theft, and these programs are becoming more prevalent.
  • Corporate AI labs justify this posturing with pseudoscientific research reports that misdirect regulatory attention to such imaginary scenarios using fear-mongering terminology, such as “existential risk.”
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  • Because the term “AI” is ambiguous, it makes having clear discussions more difficult. In one sense, it is the name of a subfield of computer science. In another, it can refer to the computing techniques developed in that subfield, most of which are now focused on pattern matching based on large data sets and the generation of new media based on those patterns. Finally, in marketing copy and start-up pitch decks, the term “AI” serves as magic fairy dust that will supercharge your business.
  • output can seem so plausible that without a clear indication of its synthetic origins, it becomes a noxious and insidious pollutant of our information ecosystem
  • Not only do we risk mistaking synthetic text for reliable information, but also that noninformation reflects and amplifies the biases encoded in its training data—in this case, every kind of bigotry exhibited on the Internet. Moreover the synthetic text sounds authoritative despite its lack of citations back to real sources. The longer this synthetic text spill continues, the worse off we are, because it gets harder to find trustworthy sources and harder to trust them when we do.
  • the people selling this technology propose that text synthesis machines could fix various holes in our social fabric: the lack of teachers in K–12 education, the inaccessibility of health care for low-income people and the dearth of legal aid for people who cannot afford lawyers, just to name a few
  • the systems rely on enormous amounts of training data that are stolen without compensation from the artists and authors who created it in the first place
  • the task of labeling data to create “guardrails” that are intended to prevent an AI system’s most toxic output from seeping out is repetitive and often traumatic labor carried out by gig workers and contractors, people locked in a global race to the bottom for pay and working conditions.
  • employers are looking to cut costs by leveraging automation, laying off people from previously stable jobs and then hiring them back as lower-paid workers to correct the output of the automated systems. This can be seen most clearly in the current actors’ and writers’ strikes in Hollywood, where grotesquely overpaid moguls scheme to buy eternal rights to use AI replacements of actors for the price of a day’s work and, on a gig basis, hire writers piecemeal to revise the incoherent scripts churned out by AI.
  • too many AI publications come from corporate labs or from academic groups that receive disproportionate industry funding. Much is junk science—it is nonreproducible, hides behind trade secrecy, is full of hype and uses evaluation methods that lack construct validity
  • We urge policymakers to instead draw on solid scholarship that investigates the harms and risks of AI—and the harms caused by delegating authority to automated systems, which include the unregulated accumulation of data and computing power, climate costs of model training and inference, damage to the welfare state and the disempowerment of the poor, as well as the intensification of policing against Black and Indigenous families. Solid research in this domain—including social science and theory building—and solid policy based on that research will keep the focus on the people hurt by this technology.
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