by crissly | Nov 4, 2022 | Uncategorized
As more and more problems with AI have surfaced, including biases around race, gender, and age, many tech companies have installed “ethical AI” teams ostensibly dedicated to identifying and mitigating such issues.
Twitter’s META unit was more progressive than most in publishing details of problems with the company’s AI systems, and in allowing outside researchers to probe its algorithms for new issues.
Last year, after Twitter users noticed that a photo-cropping algorithm seemed to favor white faces when choosing how to trim images, Twitter took the unusual decision to let its META unit publish details of the bias it uncovered. The group also launched one of the first ever “bias bounty” contests, which let outside researchers test the algorithm for other problems. Last October, Chowdhury’s team also published details of unintentional political bias on Twitter, showing how right-leaning news sources were, in fact, promoted more than left-leaning ones.
Many outside researchers saw the layoffs as a blow, not just for Twitter but for efforts to improve AI. “What a tragedy,” Kate Starbird, an associate professor at the University of Washington who studies online disinformation, wrote on Twitter.
“The META team was one of the only good case studies of a tech company running an AI ethics group that interacts with the public and academia with substantial credibility,” says Ali Alkhatib, director of the Center for Applied Data Ethics at the University of San Francisco.
Alkhatib says Chowdhury is incredibly well thought of within the AI ethics community and her team did genuinely valuable work holding Big Tech to account. “There aren’t many corporate ethics teams worth taking seriously,” he says. “This was one of the ones whose work I taught in classes.”
Mark Riedl, a professor studying AI at Georgia Tech, says the algorithms that Twitter and other social media giants use have a huge impact on people’s lives, and need to be studied. “Whether META had any impact inside Twitter is hard to discern from the outside, but the promise was there,” he says.
Riedl adds that letting outsiders probe Twitter’s algorithms was an important step toward more transparency and understanding of issues around AI. “They were becoming a watchdog that could help the rest of us understand how AI was affecting us,” he says. “The researchers at META had outstanding credentials with long histories of studying AI for social good.”
As for Musk’s idea of open-sourcing the Twitter algorithm, the reality would be far more complicated. There are many different algorithms that affect the way information is surfaced, and it’s challenging to understand them without the real time data they are being fed in terms of tweets, views, and likes.
The idea that there is one algorithm with explicit political leaning might oversimplify a system that can harbor more insidious biases and problems. Uncovering these is precisely the kind of work that Twitter’s META group was doing. “There aren’t many groups that rigorously study their own algorithms’ biases and errors,” says Alkhatib at the University of San Francisco. “META did that.” And now, it doesn’t.
by crissly | Jun 14, 2022 | Uncategorized
The uproar caused by Blake Lemoine, a Google engineer who believes that one of the company’s most sophisticated chat programs, Language Model for Dialogue Applications (LaMDA) is sapient, has had a curious element: Actual AI ethics experts are all but renouncing further discussion of the AI sapience question, or deeming it a distraction. They’re right to do so.
In reading the edited transcript Lemoine released, it was abundantly clear that LaMDA was pulling from any number of websites to generate its text; its interpretation of a Zen koan could’ve come from anywhere, and its fable read like an automatically generated story (though its depiction of the monster as “wearing human skin” was a delightfully HAL-9000 touch). There was no spark of consciousness there, just little magic tricks that paper over the cracks. But it’s easy to see how someone might be fooled, looking at social media responses to the transcript—with even some educated people expressing amazement and a willingness to believe. And so the risk here is not that the AI is truly sentient but that we are well-poised to create sophisticated machines that can imitate humans to such a degree that we cannot help but anthropomorphize them—and that large tech companies can exploit this in deeply unethical ways.
As should be clear from the way we treat our pets, or how we’ve interacted with Tamagotchi, or how we video gamers reload a save if we accidentally make an NPC cry, we are actually very capable of empathizing with the nonhuman. Imagine what such an AI could do if it was acting as, say, a therapist. What would you be willing to say to it? Even if you “knew” it wasn’t human? And what would that precious data be worth to the company that programmed the therapy bot?
It gets creepier. Systems engineer and historian Lilly Ryan warns that what she calls ecto-metadata—the metadata you leave behind online that illustrates how you think—is vulnerable to exploitation in the near future. Imagine a world where a company created a bot based on you and owned your digital “ghost” after you’d died. There’d be a ready market for such ghosts of celebrities, old friends, and colleagues. And because they would appear to us as a trusted loved one (or someone we’d already developed a parasocial relationship with) they’d serve to elicit yet more data. It gives a whole new meaning to the idea of “necropolitics.” The afterlife can be real, and Google can own it.
Just as Tesla is careful about how it markets its “autopilot,” never quite claiming that it can drive the car by itself in true futuristic fashion while still inducing consumers to behave as if it does (with deadly consequences), it is not inconceivable that companies could market the realism and humanness of AI like LaMDA in a way that never makes any truly wild claims while still encouraging us to anthropomorphize it just enough to let our guard down. None of this requires AI to be sapient, and it all preexists that singularity. Instead, it leads us into the murkier sociological question of how we treat our technology and what happens when people act as if their AIs are sapient.
In “Making Kin With the Machines,” academics Jason Edward Lewis, Noelani Arista, Archer Pechawis, and Suzanne Kite marshal several perspectives informed by Indigenous philosophies on AI ethics to interrogate the relationship we have with our machines, and whether we’re modeling or play-acting something truly awful with them—as some people are wont to do when they are sexist or otherwise abusive toward their largely feminine-coded virtual assistants. In her section of the work, Suzanne Kite draws on Lakota ontologies to argue that it is essential to recognize that sapience does not define the boundaries of who (or what) is a “being” worthy of respect.
This is the flip side of the AI ethical dilemma that’s already here: Companies can prey on us if we treat their chatbots like they’re our best friends, but it’s equally perilous to treat them as empty things unworthy of respect. An exploitative approach to our tech may simply reinforce an exploitative approach to each other, and to our natural environment. A humanlike chatbot or virtual assistant should be respected, lest their very simulacrum of humanity habituate us to cruelty toward actual humans.
Kite’s ideal is simply this: a reciprocal and humble relationship between yourself and your environment, recognizing mutual dependence and connectivity. She argues further, “Stones are considered ancestors, stones actively speak, stones speak through and to humans, stones see and know. Most importantly, stones want to help. The agency of stones connects directly to the question of AI, as AI is formed from not only code, but from materials of the earth.” This is a remarkable way of tying something typically viewed as the essence of artificiality to the natural world.
What is the upshot of such a perspective? Sci-fi author Liz Henry offers one: “We could accept our relationships to all the things in the world around us as worthy of emotional labor and attention. Just as we should treat all the people around us with respect, acknowledging they have their own life, perspective, needs, emotions, goals, and place in the world.”
This is the AI ethical dilemma that stands before us: the need to make kin of our machines weighed against the myriad ways this can and will be weaponized against us in the next phase of surveillance capitalism. Much as I long to be an eloquent scholar defending the rights and dignity of a being like Mr. Data, this more complex and messy reality is what demands our attention. After all, there can be a robot uprising without sapient AI, and we can be a part of it by liberating these tools from the ugliest manipulations of capital.
by crissly | Feb 16, 2022 | Uncategorized
para leer este articulo en español por favor aprete aqui.
In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention.
“With technology you can foresee five or six years in advance, with first name, last name, and address, which girl—future teenager—is 86 percent predestined to have an adolescent pregnancy,” Juan Manuel Urtubey, then the governor of the province, proudly declared on national television. The stated goal was to use the algorithm to predict which girls from low-income areas would become pregnant in the next five years. It was never made clear what would happen once a girl or young woman was labeled as “predestined” for motherhood or how this information would help prevent adolescent pregnancy. The social theories informing the AI system, like its algorithms, were opaque.
The system was based on data—including age, ethnicity, country of origin, disability, and whether the subject’s home had hot water in the bathroom—from 200,000 residents in the city of Salta, including 12,000 women and girls between the ages of 10 and 19. Though there is no official documentation, from reviewing media articles and two technical reviews, we know that “territorial agents” visited the houses of the girls and women in question, asked survey questions, took photos, and recorded GPS locations. What did those subjected to this intimate surveillance have in common? They were poor, some were migrants from Bolivia and other countries in South America, and others were from Indigenous Wichí, Qulla, and Guaraní communities.
Although Microsoft spokespersons proudly announced that the technology in Salta was “one of the pioneering cases in the use of AI data” in state programs, it presents little that is new. Instead, it is an extension of a long Argentine tradition: controlling the population through surveillance and force. And the reaction to it shows how grassroots Argentine feminists were able to take on this misuse of artificial intelligence.
In the 19th and early 20th centuries, successive Argentine governments carried out a genocide of Indigenous communities and promoted immigration policies based on ideologies designed to attract European settlement, all in hopes of blanquismo, or “whitening” the country. Over time, a national identity was constructed along social, cultural, and most of all racial lines.
This type of eugenic thinking has a propensity to shapeshift and adapt to new scientific paradigms and political circumstances, according to historian Marisa Miranda, who tracks Argentina’s attempts to control the population through science and technology. Take the case of immigration. Throughout Argentina’s history, opinion has oscillated between celebrating immigration as a means of “improving” the population and considering immigrants to be undesirable and a political threat to be carefully watched and managed.
More recently, the Argentine military dictatorship between 1976 and 1983 controlled the population through systematic political violence. During the dictatorship, women had the “patriotic task” of populating the country, and contraception was prohibited by a 1977 law. The cruelest expression of the dictatorship’s interest in motherhood was the practice of kidnapping pregnant women considered politically subversive. Most women were murdered after giving birth and many of their children were illegally adopted by the military to be raised by “patriotic, Catholic families.”
While Salta’s AI system to “predict pregnancy” was hailed as futuristic, it can only be understood in light of this long history, particularly, in Miranda’s words, the persistent eugenic impulse that always “contains a reference to the future” and assumes that reproduction “should be managed by the powerful.”
Due to the complete lack of national AI regulation, the Technology Platform for Social Intervention was never subject to formal review and no assessment of its impacts on girls and women has been made. There has been no official data published on its accuracy or outcomes. Like most AI systems all over the world, including those used in sensitive contexts, it lacks transparency and accountability.
Though it is unclear whether the technology program was ultimately suspended, everything we know about the system comes from the efforts of feminist activists and journalists who led what amounted to a grassroots audit of a flawed and harmful AI system. By quickly activating a well-oiled machine of community organizing, these activists brought national media attention to how an untested, unregulated technology was being used to violate the rights of girls and women.
“The idea that algorithms can predict teenage pregnancy before it happens is the perfect excuse for anti-women and anti-sexual and reproductive rights activists to declare abortion laws unnecessary,” wrote feminist scholars Paz Peña and Joana Varon at the time. Indeed, it was soon revealed that an Argentine nonprofit called the Conin Foundation, run by doctor Abel Albino, a vocal opponent of abortion rights, was behind the technology, along with Microsoft.
by crissly | Dec 17, 2021 | Uncategorized
Childcare is the most intimate of activities. Evolution has generated drives so powerful that we will risk our lives to protect not only our own children, but quite often any child, and even the young of other species. Robots, by contrast, are products created by commercial entities with commercial goals, which may—and should—include the well-being of their customers, but will never be limited to such. Robots, corporations, and other legal or non-legal entities do not possess the instinctual nature of humans to care for the young—even if our anthropomorphic tendencies may prompt some children and adults to overlook this fact.
As a result, it is important to take into account the likelihood of deception—both commercial deception through advertising and also self-deception on the part of parents—despite the fact that robots are unlikely to cause significant psychological damage to children and to others who may come to love them.
Neither television manufacturers, broadcasters, nor online game manufacturers are deemed liable when children are left for too long in front of their television. Robotics companies will want to be in the same position, as no company will want to be liable for damage to children, so it is likely that manufacturers will undersell the artificial intelligence (AI) and interactive capacities of their robots. It is therefore likely that any robots (and certainly those in jurisdictions with strong consumer protection) will be marketed primarily as toys, surveillance devices, and possibly household utilities. They will be brightly colored and deliberately designed to appeal to parents and children. We expect a variety of products, some with advanced capabilities and some with humanoid features. Parents will quickly discover a robot’s ability to engage and distract their child. Robotics companies will program
experiences geared toward parents and children, just as television broadcasters do. But robots will always have disclaimers, such as “this device is not a toy and should only be used with adult supervision” or “this device is provided for entertainment only. It should not be considered educational.”
Nevertheless, parents will notice that they can leave their children alone with robots, just as they can leave them to watch television or to play with other children. Humans are phenomenal learners and very good at detecting regularities and exploiting affordances. Parents will quickly notice the educational benefits of robot nannies that have advanced AI and communication skills. Occasional horror stories, such as the robot nanny and toddler tragedy in the novel Scarlett and Gurl, will make headline news and remind parents how to use robots responsibly.
This will likely continue until or unless the incidence of injuries necessitates redesign, a revision of consumer safety standards, statutory notice requirements, and/or risk-based uninsurability, all of which will further refine the industry. Meanwhile, the media will also seize on stories of robots saving children in unexpected ways, as it does now when children (or adults) are saved by other young children and dogs. This should not make people think that they should leave children alone with robots, but given the propensity we already have to anthropomorphize robots, it may make parents feel that little bit more comfortable—until the next horror story makes headlines.
When it comes to liability, we should be able to communicate the same model of liability applied to toys to the manufacturers of robot nannies: Make your robots reliable, describe what they do accurately, and provide sufficient notice of reasonably foreseeable danger from misuse. Then, apart from the exceptional situation of errors in design or manufacture, such as parts that come off and choke children, legal liability will rest entirely with the parent or responsible adult, as it does now, and as it should under existing product liability law.
by crissly | Oct 28, 2021 | Uncategorized
Delphi taps the fruits of recent advances in AI and language. Feeding very large amounts of text to algorithms that use mathematically simulated neural networks has yielded surprising advances.
In June 2020, researchers at OpenAI, a company working on cutting-edge AI tools, demonstrated a program called GPT-3 that can predict, summarize, and auto-generate text with what often seems like remarkable skill, although it will also spit out biased and hateful language learned from text it has read.
The researchers behind Delphi also asked ethical questions of GPT-3. They found its answers agreed with those of the crowd workers just over 50 percent of the time—little better than a coin flip.
Improving the performance of a system like Delphi will require different AI approaches, potentially including some that allow a machine to explain its reasoning and indicate when it is conflicted.
The idea of giving machines a moral code stretches back decades both in academic research and science fiction. Isaac Asimov’s famous Three Laws of Robotics popularized the idea that machines might follow human ethics, although the short stories that explored the idea highlighted contradictions in such simplistic reasoning.
Choi says Delphi should not be taken as providing a definitive answer to any ethical questions. A more sophisticated version might flag uncertainty, because of divergent opinions in its training data. “Life is full of gray areas,” she says. “No two human beings will completely agree, and there’s no way an AI program can match people’s judgments.”
Other machine learning systems have displayed their own moral blind spots. In 2016, Microsoft released a chatbot called Tay designed to learn from online conversations. The program was quickly sabotaged and taught to say offensive and hateful things.
Efforts to explore ethical perspectives related to AI have also revealed the complexity of such a task. A project launched in 2018 by researchers at MIT and elsewhere sought to explore the public’s view of ethical conundrums that might be faced by self-driving cars. They asked people to decide, for example, whether it would be better for a vehicle to hit an elderly person, a child, or a robber. The project revealed differing opinions across different countries and social groups. Those from the US and Western Europe were more likely than respondents elsewhere to spare the child over an older person.
Some of those building AI tools are keen to engage with the ethical challenges. “I think people are right to point out the flaws and failures of the model,” says Nick Frost, CEO of Cohere, a startup that has developed a large language model that is accessible to others via an API. “They are informative of broader, wider problems.”
Cohere devised ways to guide the output of its algorithms, which are now being tested by some businesses. It curates the content that is fed to the algorithm and trains the algorithm to learn to catch instances of bias or hateful language.
Frost says the debate around Delphi reflects a broader question that the tech industry is wrestling with—how to build technology responsibly. Too often, he says, when it comes to content moderation, misinformation, and algorithmic bias, companies try to wash their hands of the problem by arguing that all technology can be used for good and bad.
When it comes to ethics, “there’s no ground truth, and sometimes tech companies abdicate responsibility because there’s no ground truth,” Frost says. “The better approach is to try.”
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