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NPR's Ailsa Chang speaks with scholar Safiya Noble about how advancements in artificial intelligence could further perpetuate biases in society.

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NPR's Ailsa Chang speaks with scholar Safiya Noble about how advancements in artificial intelligence could further perpetuate biases in society.

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NPR's Ailsa Chang speaks with scholar Safiya Noble about how advancements in artificial intelligence could further perpetuate biases in society.

AILSA CHANG, HOST:

AI, or artificial intelligence, is the phrase of the moment, right? From politicians to scientists to tech billionaires, it seems like everyone is scrambling to better understand how this new technology can impact all of our daily lives - for better or for worse. And right now, there does seem to be more questions than answers. For example, one concern - how the massive amount of information gathered by AI and incorporated into other technologies could perpetuate racism, sexism and other biases. To better understand that issue of racial bias, I spoke with Safiya Noble, an internet studies scholar and professor of gender studies and African American studies at UCLA. She's the author of the book, "Algorithms Of Oppression: How Search Engines Reinforce Racism." And I asked her how she thinks artificial intelligence can be used to sustain racist systems.

SAFIYA NOBLE: So the thing that keeps me up at night most is things like machine learning, which is really when we give instructions to computers to look for anything it wants - this kind of pulling in of lots of different kinds of data, looking for patterns and making predictions around a set of outcomes. This is really the way that machine learning works. It's where a machine is pulling in more information than the human mind necessarily can do in a...

CHANG: Yeah.

NOBLE: ...Very short period of time and then kind of culling through that and spitting out predictions. Now, we see this kind of machine learning in the topic of the day right now, which is ChatGPT.

CHANG: Right.

NOBLE: That's all a predictive text analytic. It's not a human being or a set of people or experts who are really culling through with a type of understanding to help us, you know, make sense of what we're going to receive as an output from those types of technologies. I'm concerned about that, and I'm concerned about the way in which machine learning and predictive analytics are both overdetermining certain kinds of outcomes.

CHANG: Give us some specific ways that biased AI is affecting people's lives right now.

NOBLE: Well, one of the things we know, probably - and I will start with what I think of as the most life or death dimensions of AI - biased AI, discriminatory AI - would be the use of AI in things like criminal sentencing and, you know, determining whether a person is likely to be a risk or not and keeping them in prison or in jail or releasing them on bail or releasing them entirely.

We saw from the very important research done by Julia Angwin and her team around the COMPAS recidivism prediction software a couple of years ago how Black people who were charged with crimes were more than four times likely to be sentenced to very severe punishment, as opposed to white offenders who were committing violent crimes and were much more likely to be released on bail.

CHANG: And why is that? What kind of information is being fed into AI to determine these sentences that would be inherently biased against Black people?

NOBLE: What is used to determine these kinds of predictive AIs are things like histories of arrests in a certain zip code. So if you live in a zip code that has been overpoliced historically, you are going to have overarresting. And we know that the overpolicing and the overarresting happens in Black and Latino communities. That's just a fact. So if that is a main factor in whether you are likely to commit - in predicting whether you're likely to commit another crime because lots of people in the zip code you live in have been arrested more than, let's say, you know, in South Central LA where I live versus in Beverly Hills, then you are more likely to be considered a risk. That has nothing to do with you. That has to do with the history of structural racism in policing in the United States.

CHANG: What do you think is needed to ensure that AI systems do not further contribute to systemic racism? What would you suggest?

NOBLE: I think we need a very robust digital civil rights agenda in this country. And, of course, many other scholars and activists have been concerned about the way in which our civil and human rights are constantly encroached upon by these different kinds of technologies, as well as other kinds of rights - you know, copyright protections, intellectual property rights. We have a massive diversity problem when it comes to racial and ethnic diversity. We have a total exclusion, for the most part, of Black and Latino/Latina workers, Indigenous workers and women of color. So there's no question that, if you had a more diverse workforce, you might detect some of these things. You might ask different questions. You might ask some of the harder questions. But fundamentally, we have to have a robust human and civil rights framework for evaluating these technologies. And I think, you know, they shouldn't be allowed into the marketplace to propagate harm, and then we find out after the fact that they are dangerous and we have to do the work of trying to recall them. That would be a very powerful start.

CHANG: Safiya Noble, internet studies scholar and professor of gender studies and African American studies at UCLA. Thank you so much for this conversation.

NOBLE: Thank you, Ailsa.

(SOUNDBITE OF GNARLS BARKLEY'S "CRAZY (INSTRUMENTAL)")

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How AI could perpetuate racism, sexism and other biases in society

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NPR's Ailsa Chang speaks with scholar Safiya Noble about how advancements in artificial intelligence could further perpetuate biases in society.

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As the technology becomes ubiquitous, a vast tasker underclass is emerging — and not going anywhere.

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As the technology becomes ubiquitous, a vast tasker underclass is emerging - and not going anywhere.

Illustrations by Richard Parry for The Verge

A few months after graduating from college in Nairobi, a 30-year-old I'll call Joe got a job as an annotator - the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. In Joe's case, he was labeling footage for self-driving cars - identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of - frame by frame and from every possible camera angle. It's difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.

Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. Over half of Joe's students usually dropped out before the boot camp was finished. "Some people don't know how to stay in one place for long," he explained with gracious understatement. Also, he acknowledged, "it is very boring."

But it was a job in a place where jobs were scarce, and Joe turned out hundreds of graduates. After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn't really a problem because they rarely knew themselves. Labeling objects for self-driving cars was obvious, but what about categorizing whether snippets of distorted dialogue were spoken by a robot or a human? Uploading photos of yourself staring into a webcam with a blank expression, then with a grin, then wearing a motorcycle helmet? Each project was such a small component of some larger process that it was difficult to say what they were actually training AI to do. Nor did the names of the projects offer any clues: Crab Generation, Whale Segment, Woodland Gyro, and Pillbox Bratwurst. They were non sequitur code names for non sequitur work.

As for the company employing them, most knew it only as Remotasks, a website offering work to anyone fluent in English. Like most of the annotators I spoke with, Joe was unaware until I told him that Remotasks is the worker-facing subsidiary of a company called Scale AI, a multibillion-dollar Silicon Valley data vendor that counts OpenAI and the U.S. military among its customers. Neither Remotasks' or Scale's website mentions the other.

Much of the public response to language models like OpenAI's ChatGPT has focused on all the jobs they appear poised to automate. But behind even the most impressive AI system are people - huge numbers of people labeling data to train it and clarifying data when it gets confused. Only the companies that can afford to buy this data can compete, and those that get it are highly motivated to keep it secret. The result is that, with few exceptions, little is known about the information shaping these systems' behavior, and even less is known about the people doing the shaping.

For Joe's students, it was work stripped of all its normal trappings: a schedule, colleagues, knowledge of what they were working on or whom they were working for. In fact, they rarely called it work at all - just "tasking." They were taskers.

The anthropologist David Graeber defines "bullshit jobs" as employment without meaning or purpose, work that should be automated but for reasons of bureaucracy or status or inertia is not. These AI jobs are their bizarro twin: work that people want to automate, and often think is already automated, yet still requires a human stand-in. The jobs have a purpose; it's just that workers often have no idea what it is.

The current AI boom - the convincingly human-sounding chatbots, the artwork that can be generated from simple prompts, and the multibillion-dollar valuations of the companies behind these technologies - began with an unprecedented feat of tedious and repetitive labor.

In 2007, the AI researcher Fei-Fei Li, then a professor at Princeton, suspected the key to improving image-recognition neural networks, a method of machine learning that had been languishing for years, was training on more data - millions of labeled images rather than tens of thousands. The problem was that it would take decades and millions of dollars for her team of undergrads to label that many photos.

Li found thousands of workers on Mechanical Turk, Amazon's crowdsourcing platform where people around the world complete small tasks for cheap. The resulting annotated dataset, called ImageNet, enabled breakthroughs in machine learning that revitalized the field and ushered in a decade of progress.

Annotation remains a foundational part of making AI, but there is often a sense among engineers that it's a passing, inconvenient prerequisite to the more glamorous work of building models. You collect as much labeled data as you can get as cheaply as possible to train your model, and if it works, at least in theory, you no longer need the annotators. But annotation is never really finished. Machine-learning systems are what researchers call "brittle," prone to fail when encountering something that isn't well represented in their training data. These failures, called "edge cases," can have serious consequences. In 2018, an Uber self-driving test car killed a woman because, though it was programmed to avoid cyclists and pedestrians, it didn't know what to make of someone walking a bike across the street. The more AI systems are put out into the world to dispense legal advice and medical help, the more edge cases they will encounter and the more humans will be needed to sort them. Already, this has given rise to a global industry staffed by people like Joe who use their uniquely human faculties to help the machines.

Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a "decorative bowl" if it's full of apples? What color is leopard print?

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don't get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors.

"There's an entire supply chain," said Sonam Jindal, the program and research lead of the nonprofit Partnership on AI. "The general perception in the industry is that this work isn't a critical part of development and isn't going to be needed for long. All the excitement is around building artificial intelligence, and once we build that, it won't be needed anymore, so why think about it? But it's infrastructure for AI. Human intelligence is the basis of artificial intelligence, and we need to be valuing these as real jobs in the AI economy that are going to be here for a while."

The data vendors behind familiar names like OpenAI, Google, and Microsoft come in different forms. There are private outsourcing companies with call-center-like offices, such as the Kenya- and Nepal-based CloudFactory, where Joe annotated for $1.20 an hour before switching to Remotasks. There are also "crowdworking" sites like Mechanical Turk and Clickworker where anyone can sign up to perform tasks. In the middle are services like Scale AI. Anyone can sign up, but everyone has to pass qualification exams and training courses and undergo performance monitoring. Annotation is big business. Scale, founded in 2016 by then-19-year-old Alexandr Wang, was valued in 2021 at $7.3 billion, making him what Forbes called "the youngest self-made billionaire," though the magazine noted in a recent profile that his stake has fallen on secondary markets since then.

This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don't have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of "millions" with the potential to become "billions."

Automation often unfolds in unexpected ways. Erik Duhaime, CEO of medical-data-annotation company Centaur Labs, recalled how, several years ago, prominent machine-learning engineers were predicting AI would make the job of radiologist obsolete. When that didn't happen, conventional wisdom shifted to radiologists using AI as a tool. Neither of those is quite what he sees occurring. AI is very good at specific tasks, Duhaime said, and that leads work to be broken up and distributed across a system of specialized algorithms and to equally specialized humans. An AI system might be capable of spotting cancer, he said, giving a hypothetical example, but only in a certain type of imagery from a certain type of machine; so now, you need a human to check that the AI is being fed the right type of data and maybe another human who checks its work before passing it to another AI that writes a report, which goes to another human, and so on. "AI doesn't replace work," he said. "But it does change how work is organized."

You might miss this if you believe AI is a brilliant, thinking machine. But if you pull back the curtain even a little, it looks more familiar, the latest iteration of a particularly Silicon Valley division of labor, in which the futuristic gleam of new technologies hides a sprawling manufacturing apparatus and the people who make it run. Duhaime reached back farther for a comparison, a digital version of the transition from craftsmen to industrial manufacturing: coherent processes broken into tasks and arrayed along assembly lines with some steps done by machines and some by humans but none resembling what came before.

Worries about AI-driven disruption are often countered with the argument that AI automates tasks, not jobs, and that these tasks will be the dull ones, leaving people to pursue more fulfilling and human work. But just as likely, the rise of AI will look like past labor-saving technologies, maybe like the telephone or typewriter, which vanquished the drudgery of message delivering and handwriting but generated so much new correspondence, commerce, and paperwork that new offices staffed by new types of workers - clerks, accountants, typists - were required to manage it. When AI comes for your job, you may not lose it, but it might become more alien, more isolating, more tedious.

Earlier this year, I signed up for Scale AI's Remotasks. The process was straightforward. After entering my computer specs, internet speed, and some basic contact information, I found myself in the "training center." To access a paying task, I first had to complete an associated (unpaid) intro course.

The training center displayed a range of courses with inscrutable names like Glue Swimsuit and Poster Macadamia. I clicked on something called GFD Chunking, which revealed itself to be labeling clothing in social-media photos.

The instructions, however, were odd. For one, they basically consisted of the same direction reiterated in the idiosyncratically colored and capitalized typography of a collaged bomb threat.

"DO LABEL items that are real and can be worn by humans or are intended to be worn by real people," it read.

"All items below SHOULD be labeled because they are real and can be worn by real-life humans," it reiterated above photos of an Air Jordans ad, someone in a Kylo Ren helmet, and mannequins in dresses, over which was a lime-green box explaining, once again, "DO Label real items that can be worn by real people."

I skimmed to the bottom of the manual, where the instructor had written in the large bright-red font equivalent of grabbing someone by the shoulders and shaking them, "THE FOLLOWING ITEMS SHOULD NOT BE LABELED because a human could not actually put wear any of these items!" above a photo of C-3PO, Princess Jasmine from Aladdin, and a cartoon shoe with eyeballs.

Feeling confident in my ability to distinguish between real clothes that can be worn by real people and not-real clothes that cannot, I proceeded to the test. Right away, it threw an ontological curveball: a picture of a magazine depicting photos of women in dresses. Is a photograph of clothing real clothing? No, I thought, because a human cannot wear a photograph of clothing. Wrong! As far as AI is concerned, photos of real clothes are real clothes. Next came a photo of a woman in a dimly lit bedroom taking a selfie before a full-length mirror. The blouse and shorts she's wearing are real. What about their reflection? Also real! Reflections of real clothes are also real clothes.

After an embarrassing amount of trial and error, I made it to the actual work, only to make the horrifying discovery that the instructions I'd been struggling to follow had been updated and clarified so many times that they were now a full 43 printed pages of directives: Do NOT label open suitcases full of clothes; DO label shoes but do NOT label flippers; DO label leggings but do NOT label tights; do NOT label towels even if someone is wearing it; label costumes but do NOT label armor. And so on.

There has been general instruction disarray across the industry, according to Milagros Miceli, a researcher at the Weizenbaum Institute in Germany who studies data work. It is in part a product of the way machine-learning systems learn. Where a human would get the concept of "shirt" with a few examples, machine-learning programs need thousands, and they need to be categorized with perfect consistency yet varied enough (polo shirts, shirts being worn outdoors, shirts hanging on a rack) that the very literal system can handle the diversity of the real world. "Imagine simplifying complex realities into something that is readable for a machine that is totally dumb," she said.

Once, Victor stayed up 36 hours straight labeling elbows and knees and heads in photographs of crowds - he has no idea why.

The act of simplifying reality for a machine results in a great deal of complexity for the human. Instruction writers must come up with rules that will get humans to categorize the world with perfect consistency. To do so, they often create categories no human would use. A human asked to tag all the shirts in a photo probably wouldn't tag the reflection of a shirt in a mirror because they would know it is a reflection and not real. But to the AI, which has no understanding of the world, it's all just pixels and the two are perfectly identical. Fed a dataset with some shirts labeled and other (reflected) shirts unlabeled, the model won't work. So the engineer goes back to the vendor with an update: DO label reflections of shirts. Soon, you have a 43-page guide descending into red all-caps.

"When you start off, the rules are relatively simple," said a former Scale employee who requested anonymity because of an NDA. "Then they get back a thousand images and then they're like, Wait a second, and then you have multiple engineers and they start to argue with each other. It's very much a human thing."

The job of the annotator often involves putting human understanding aside and following instructions very, very literally - to think, as one annotator said, like a robot. It's a strange mental space to inhabit, doing your best to follow nonsensical but rigorous rules, like taking a standardized test while on hallucinogens. Annotators invariably end up confronted with confounding questions like, Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a "decorative bowl" if it's full of apples? What color is leopard print? When instructors said to label traffic-control directors, did they also mean to label traffic-control directors eating lunch on the sidewalk? Every question must be answered, and a wrong guess could get you banned and booted to a new, totally different task with its own baffling rules.

Most of the work on Remotasks is paid at a piece rate with a single task earning anywhere from a few cents to several dollars. Because tasks can take seconds or hours, wages are hard to predict. When Remotasks first arrived in Kenya, annotators said it paid relatively well - averaging about $5 to $10 per hour depending on the task - but the amount fell as time went on.

Scale AI spokesperson Anna Franko said that the company's economists analyze the specifics of a project, the skills required, the regional cost of living, and other factors "to ensure fair and competitive compensation." Former Scale employees also said pay is determined through a surge-pricing-like mechanism that adjusts for how many annotators are available and how quickly the data is needed.

According to workers I spoke with and job listings, U.S.-based Remotasks annotators generally earn between $10 and $25 per hour, though some subject-matter experts can make more. By the beginning of this year, pay for the Kenyan annotators I spoke with had dropped to between $1 and $3 per hour.

That is, when they were making any money at all. The most common complaint about Remotasks work is its variability; it's steady enough to be a full-time job for long stretches but too unpredictable to rely on. Annotators spend hours reading instructions and completing unpaid trainings only to do a dozen tasks and then have the project end. There might be nothing new for days, then, without warning, a totally different task appears and could last anywhere from a few hours to weeks. Any task could be their last, and they never know when the next one will come.

This boom-and-bust cycle results from the cadence of AI development, according to engineers and data vendors. Training a large model requires an enormous amount of annotation followed by more iterative updates, and engineers want it all as fast as possible so they can hit their target launch date. There may be monthslong demand for thousands of annotators, then for only a few hundred, then for a dozen specialists of a certain type, and then thousands again. "The question is, Who bears the cost for these fluctuations?" said Jindal of Partnership on AI. "Because right now, it's the workers."

"I really am wasting my life here if I made somebody a billionaire and I'm earning a couple of bucks a week."

To succeed, annotators work together. When I told Victor, who started working for Remotasks while at university in Nairobi, about my struggles with the traffic-control-directors task, he told me everyone knew to stay away from that one: too tricky, bad pay, not worth it. Like a lot of annotators, Victor uses unofficial WhatsApp groups to spread the word when a good task drops. When he figures out a new one, he starts impromptu Google Meets to show others how it's done. Anyone can join and work together for a time, sharing tips. "It's a culture we have developed of helping each other because we know when on your own, you can't know all the tricks," he said.

Because work appears and vanishes without warning, taskers always need to be on alert. Victor has found that projects pop up very late at night, so he is in the habit of waking every three hours or so to check his queue. When a task is there, he'll stay awake as long as he can to work. Once, he stayed up 36 hours straight labeling elbows and knees and heads in photographs of crowds - he has no idea why. Another time, he stayed up so long his mother asked him what was wrong with his eyes. He looked in the mirror to discover they were swollen.

Annotators generally know only that they are training AI for companies located vaguely elsewhere, but sometimes the veil of anonymity drops - instructions mentioning a brand or a chatbot say too much. "I read and I Googled and found I am working for a 25-year-old billionaire," said one worker, who, when we spoke, was labeling the emotions of people calling to order Domino's pizza. "I really am wasting my life here if I made somebody a billionaire and I'm earning a couple of bucks a week."

Victor is a self-proclaimed "fanatic" about AI and started annotating because he wants to help bring about a fully automated post-work future. But earlier this year, someone dropped a Time story into one of his WhatsApp groups about workers training ChatGPT to recognize toxic content who were getting paid less than $2 an hour by the vendor Sama AI. "People were angry that these companies are so profitable but paying so poorly," Victor said. He was unaware until I told him about Remotasks' connection to Scale. Instructions for one of the tasks he worked on were nearly identical to those used by OpenAI, which meant he had likely been training ChatGPT as well, for approximately $3 per hour.

"I remember that someone posted that we will be remembered in the future," he said. "And somebody else replied, 'We are being treated worse than foot soldiers. We will be remembered nowhere in the future.' I remember that very well. Nobody will recognize the work we did or the effort we put in."

Identifying clothing and labeling customer-service conversations are just some of the annotation gigs available. Lately, the hottest on the market has been chatbot trainer. Because it demands specific areas of expertise or language fluency and wages are often adjusted regionally, this job tends to pay better. Certain types of specialist annotation can go for $50 or more per hour.

A woman I'll call Anna was searching for a job in Texas when she stumbled across a generic listing for online work and applied. It was Remotasks, and after passing an introductory exam, she was brought into a Slack room of 1,500 people who were training a project code-named Dolphin, which she later discovered to be Google DeepMind's chatbot, Sparrow, one of the many bots competing with ChatGPT. Her job is to talk with it all day. At about $14 an hour, plus bonuses for high productivity, "it definitely beats getting paid $10 an hour at the local Dollar General store," she said.

Also, she enjoys it. She has discussed science-fiction novels, mathematical paradoxes, children's riddles, and TV shows. Sometimes the bot's responses make her laugh; other times, she runs out of things to talk about. "Some days, my brain is just like, I literally have no idea what on earth to ask it now, " she said. "So I have a little notebook, and I've written about two pages of things - I just Google interesting topics - so I think I'll be good for seven hours today, but that's not always the case."

Each time Anna prompts Sparrow, it delivers two responses and she picks the best one, thereby creating something called "human-feedback data." When ChatGPT debuted late last year, its impressively natural-seeming conversational style was credited to its having been trained on troves of internet data. But the language that fuels ChatGPT and its competitors is filtered through several rounds of human annotation. One group of contractors writes examples of how the engineers want the bot to behave, creating questions followed by correct answers, descriptions of computer programs followed by functional code, and requests for tips on committing crimes followed by polite refusals. After the model is trained on these examples, yet more contractors are brought in to prompt it and rank its responses. This is what Anna is doing with Sparrow. Exactly which criteria the raters are told to use varies - honesty, or helpfulness, or just personal preference. The point is that they are creating data on human taste, and once there's enough of it, engineers can train a second model to mimic their preferences at scale, automating the ranking process and training their AI to act in ways humans approve of. The result is a remarkably human-seeming bot that mostly declines harmful requests and explains its AI nature with seeming self-awareness.

Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing.

This circuitous technique is called "reinforcement learning from human feedback," or RLHF, and it's so effective that it's worth pausing to fully register what it doesn't do. When annotators teach a model to be accurate, for example, the model isn't learning to check answers against logic or external sources or about what accuracy as a concept even is. The model is still a text-prediction machine mimicking patterns in human writing, but now its training corpus has been supplemented with bespoke examples, and the model has been weighted to favor them. Maybe this results in the model extracting patterns from the part of its linguistic map labeled as accurate and producing text that happens to align with the truth, but it can also result in it mimicking the confident style and expert jargon of the accurate text while writing things that are totally wrong. There is no guarantee that the text the labelers marked as accurate is in fact accurate, and when it is, there is no guarantee that the model learns the right patterns from it.

This dynamic makes chatbot annotation a delicate process. It has to be rigorous and consistent because sloppy feedback, like marking material that merely sounds correct as accurate, risks training models to be even more convincing bullshitters. An early OpenAI and DeepMind joint project using RLHF, in this case to train a virtual robot hand to grab an item, resulted in also training the robot to position its hand between the object and its raters and wiggle around such that it only appeared to its human overseers to grab the item. Ranking a language model's responses is always going to be somewhat subjective because it's language. A text of any length will have multiple elements that could be right or wrong or, taken together, misleading. OpenAI researchers ran into this obstacle in another early RLHF paper. Trying to get their model to summarize text, the researchers found they agreed only 60 percent of the time that a summary was good. "Unlike many tasks in [machine learning] our queries do not have unambiguous ground truth," they lamented.

When Anna rates Sparrow's responses, she's supposed to be looking at their accuracy, helpfulness, and harmlessness while also checking that the model isn't giving medical or financial advice or anthropomorphizing itself or running afoul of other criteria. To be useful training data, the model's responses have to be quantifiably ranked against one another: Is a bot that helpfully tells you how to make a bomb "better" than a bot that's so harmless it refuses to answer any questions? In one DeepMind paper, when Sparrow's makers took a turn annotating, four researchers wound up debating whether their bot had assumed the gender of a user who asked it for relationship advice. According to Geoffrey Irving, one of DeepMind's research scientists, the company's researchers hold weekly annotation meetings in which they rerate data themselves and discuss ambiguous cases, consulting with ethical or subject-matter experts when a case is particularly tricky.

There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads.

Anna often finds herself having to choose between two bad options. "Even if they're both absolutely, ridiculously wrong, you still have to figure out which one is better and then write words explaining why," she said. Sometimes, when both responses are bad, she's encouraged to write a better response herself, which she does about half the time.

Because feedback data is difficult to collect, it fetches a higher price. Basic preferences of the sort Anna is producing sell for about $1 each, according to people with knowledge of the industry. But if you want to train a model to do legal research, you need someone with training in law, and this gets expensive. Everyone involved is reluctant to say how much they're spending, but in general, specialized written examples can go for hundreds of dollars, while expert ratings can cost $50 or more. One engineer told me about buying examples of Socratic dialogues for up to $300 a pop. Another told me about paying $15 for a "darkly funny limerick about a goldfish."

OpenAI, Microsoft, Meta, and Anthropic did not comment about how many people contribute annotations to their models, how much they are paid, or where in the world they are located. Irving of DeepMind, which is a subsidiary of Google, said the annotators working on Sparrow are paid "at least the hourly living wage" based on their location. Anna knows "absolutely nothing" about Remotasks, but Sparrow has been more open. She wasn't the only annotator I spoke with who got more information from the AI they were training than from their employer; several others learned whom they were working for by asking their AI for its company's terms of service. "I literally asked it, 'What is your purpose, Sparrow?'" Anna said. It pulled up a link to DeepMind's website and explained that it's an AI assistant and that its creators trained it using RLHF to be helpful and safe.

Until recently, it was relatively easy to spot bad output from a language model. It looked like gibberish. But this gets harder as the models get better - a problem called "scalable oversight." Google inadvertently demonstrated how hard it is to catch the errors of a modern-language model when one made it into the splashy debut of its AI assistant, Bard. (It stated confidently that the James Webb Space Telescope "took the very first pictures of a planet outside of our own solar system," which is wrong.) This trajectory means annotation increasingly requires specific skills and expertise.

Last year, someone I'll call Lewis was working on Mechanical Turk when, after completing a task, he received a message inviting him to apply for a platform he hadn't heard of. It was called Taskup.ai, and its website was remarkably basic: just a navy background with text reading GET PAID FOR TASKS ON DEMAND. He applied.

The work paid far better than anything he had tried before, often around $30 an hour. It was more challenging, too: devising complex scenarios to trick chatbots into giving dangerous advice, testing a model's ability to stay in character, and having detailed conversations about scientific topics so technical they required extensive research. He found the work "satisfying and stimulating." While checking one model's attempts to code in Python, Lewis was learning too. He couldn't work for more than four hours at a stretch, lest he risk becoming mentally drained and making mistakes, and he wanted to keep the job.

"If there was one thing I could change, I would just like to have more information about what happens on the other end," he said. "We only know as much as we need to know to get work done, but if I could know more, then maybe I could get more established and perhaps pursue this as a career."

I spoke with eight other workers, most based in the U.S., who had similar experiences of answering surveys or completing tasks on other platforms and finding themselves recruited for Taskup.ai or several similarly generic sites, such as DataAnnotation.tech or Gethybrid.io. Often their work involved training chatbots, though with higher-quality expectations and more specialized purposes than other sites they had worked for. One was demonstrating spreadsheet macros. Another was just supposed to have conversations and rate responses according to whatever criteria she wanted. She often asked the chatbot things that had come up in conversations with her 7-year-old daughter, like "What is the largest dinosaur?" and "Write a story about a tiger." "I haven't fully gotten my head around what they're trying to do with it," she told me.

Taskup.ai, DataAnnotation.tech, and Gethybrid.io all appear to be owned by the same company: Surge AI. Its CEO, Edwin Chen, would neither confirm nor deny the connection, but he was willing to talk about his company and how he sees annotation evolving.

"I've always felt the annotation landscape is overly simplistic," Chen said over a video call from Surge's office. He founded Surge in 2020 after working on AI at Google, Facebook, and Twitter convinced him that crowdsourced labeling was inadequate. "We want AI to tell jokes or write really good marketing copy or help me out when I need therapy or whatnot," Chen said. "You can't ask five people to independently come up with a joke and combine it into a majority answer. Not everybody can tell a joke or solve a Python program. The annotation landscape needs to shift from this low-quality, low-skill mind-set to something that's much richer and captures the range of human skills and creativity and values that we want AI systems to possess."

Last year, Surge relabeled Google's dataset classifying Reddit posts by emotion. Google had stripped each post of context and sent them to workers in India for labeling. Surge employees familiar with American internet culture found that 30 percent of the labels were wrong. Posts like "hell yeah my brother" had been classified as annoyance and "Yay, cold McDonald's. My favorite" as love.

Surge claims to vet its workers for qualifications - that people doing creative-writing tasks have experience with creative writing, for example - but exactly how Surge finds workers is "proprietary," Chen said. As with Remotasks, workers often have to complete training courses, though unlike Remotasks, they are paid for it, according to the annotators I spoke with. Having fewer, better-trained workers producing higher-quality data allows Surge to compensate better than its peers, Chen said, though he declined to elaborate, saying only that people are paid "fair and ethical wages." The workers I spoke with earned between $15 and $30 per hour, but they are a small sample of all the annotators, a group Chen said now consists of 100,000 people. The secrecy, he explained, stems from clients' demands for confidentiality.

Surge's customers include OpenAI, Google, Microsoft, Meta, and Anthropic. Surge specializes in feedback and language annotation, and after ChatGPT launched, it got an influx of requests, Chen said: "I thought everybody knew the power of RLHF, but I guess people just didn't viscerally understand."

The new models are so impressive they've inspired another round of predictions that annotation is about to be automated. Given the costs involved, there is significant financial pressure to do so. Anthropic, Meta, and other companies have recently made strides in using AI to drastically reduce the amount of human annotation needed to guide models, and other developers have started using GPT-4 to generate training data. However, a recent paper found that GPT-4-trained models may be learning to mimic GPT's authoritative style with even less accuracy, and so far, when improvements in AI have made one form of annotation obsolete, demand for other, more sophisticated types of labeling has gone up. This debate spilled into the open earlier this year, when Scale's CEO, Wang, tweeted that he predicted AI labs will soon be spending as many billions of dollars on human data as they do on computing power; OpenAI's CEO, Sam Altman, responded that data needs will decrease as AI improves.

"I mean, what it can do is amazing," she said of the chatbot. "But it still does some really weird shit."

Chen is skeptical AI will reach a point where human feedback is no longer needed, but he does see annotation becoming more difficult as models improve. Like many researchers, he believes the path forward will involve AI systems helping humans oversee other AI. Surge recently collaborated with Anthropic on a proof of concept, having human labelers answer questions about a lengthy text with the help of an unreliable AI assistant, on the theory that the humans would have to feel out the weaknesses of their AI assistant and collaborate to reason their way to the correct answer. Another possibility has two AIs debating each other and a human rendering the final verdict on which is correct. "We still have yet to see really good practical implementations of this stuff, but it's starting to become necessary because it's getting really hard for labelers to keep up with the models," said OpenAI research scientist John Schulman in a recent talk at Berkeley.

"I think you always need a human to monitor what AIs are doing just because they are this kind of alien entity," Chen said. Machine-learning systems are just too strange ever to fully trust. The most impressive models today have what, to a human, seems like bizarre weaknesses, he added, pointing out that though GPT-4 can generate complex and convincing prose, it can't pick out which words are adjectives: "Either that or models get so good that they're better than humans at all things, in which case, you reach your utopia and who cares?"

As 2022 ended, Joe started hearing from his students that their task queues were often empty. Then he got an email informing him the boot camps in Kenya were closing. He continued training taskers online, but he began to worry about the future.

"There were signs that it was not going to last long," he said. Annotation was leaving Kenya. From colleagues he had met online, he heard tasks were going to Nepal, India, and the Philippines. "The companies shift from one region to another," Joe said. "They don't have infrastructure locally, so it makes them flexible to shift to regions that favor them in terms of operation cost."

One way the AI industry differs from manufacturers of phones and cars is in its fluidity. The work is constantly changing, constantly getting automated away and replaced with new needs for new types of data. It's an assembly line but one that can be endlessly and instantly reconfigured, moving to wherever there is the right combination of skills, bandwidth, and wages.

Lately, the best-paying work is in the U.S. In May, Scale started listing annotation jobs on its own website, soliciting people with experience in practically every field AI is predicted to conquer. There were listings for AI trainers with expertise in health coaching, human resources, finance, economics, data science, programming, computer science, chemistry, biology, accounting, taxes, nutrition, physics, travel, K-12 education, sports journalism, and self-help. You can make $45 an hour teaching robots law or make $25 an hour teaching them poetry. There were also listings for people with security clearance, presumably to help train military AI. Scale recently launched a defense-oriented language model called Donovan, which Wang called "ammunition in the AI war," and won a contract to work on the Army's robotic-combat-vehicle program.

Anna is still training chatbots in Texas. Colleagues have been turned into reviewers and Slack admins - she isn't sure why, but it has given her hope that the gig could be a longer-term career. One thing she isn't worried about is being automated out of a job. "I mean, what it can do is amazing," she said of the chatbot. "But it still does some really weird shit."

When Remotasks first arrived in Kenya, Joe thought annotation could be a good career. Even after the work moved elsewhere, he was determined to make it one. There were thousands of people in Nairobi who knew how to do the work, he reasoned - he had trained many of them, after all. Joe rented office space in the city and began sourcing contracts: a job annotating blueprints for a construction company, another labeling fruits despoiled by insects for some sort of agricultural project, plus the usual work of annotating for self-driving cars and e-commerce.

But he has found his vision difficult to achieve. He has just one full-time employee, down from two. "We haven't been having a consistent flow of work," he said. There are weeks with nothing to do because customers are still collecting data, and when they're done, he has to bring in short-term contractors to meet their deadlines: "Clients don't care whether we have consistent work or not. So long as the datasets have been completed, then that's the end of that."

Rather than let their skills go to waste, other taskers decided to chase the work wherever it went. They rented proxy servers to disguise their locations and bought fake IDs to pass security checks so they could pretend to work from Singapore, the Netherlands, Mississippi, or wherever the tasks were flowing. It's a risky business. Scale has become increasingly aggressive about suspending accounts caught disguising their location, according to multiple taskers. It was during one of these crackdowns that my account got banned, presumably because I had been using a VPN to see what workers in other countries were seeing, and all $1.50 or so of my earnings were seized.

"These days, we have become a bit cunning because we noticed that in other countries they are paying well," said Victor, who was earning double the Kenyan rate by tasking in Malaysia. "You do it cautiously."

Another Kenyan annotator said that after his account got suspended for mysterious reasons, he decided to stop playing by the rules. Now, he runs multiple accounts in multiple countries, tasking wherever the pay is best. He works fast and gets high marks for quality, he said, thanks to ChatGPT. The bot is wonderful, he said, letting him speed through $10 tasks in a matter of minutes. When we spoke, he was having it rate another chatbot's responses according to seven different criteria, one AI training the other.

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How many humans does it take to make tech seem human? Millions to support OpenAI, Google, Meta, and every other major tech company. As AI becomes ubiquitous, a vast tasker underclass is emerging - and not going anywhere.

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Artificial Intelligence Has a Problem With Gender and Racial Bias. Here’s How to Solve It

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Artificial Intelligence Has a Problem With Gender and Racial Bias. Here’s How to Solve It

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Artificial Intelligence Has a Problem With Gender and Racial Bias

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Don’t Ban ChatGPT in Schools. Teach With It.

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Don’t Ban ChatGPT in Schools. Teach With It.

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OpenAI’s new chatbot is raising fears of cheating on homework, but its potential as an educational tool outweighs its risks

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Don’t Ban ChatGPT in Schools. Teach With It.

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OpenAI’s new chatbot is raising fears of cheating on homework, but its potential as an educational tool outweighs its risks.

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Pew Research Center. What the data says about Americans’ views of artificial intelligence

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Pew Research Center. What the data says about Americans’ views of artificial intelligence

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BY MICHELLE FAVERIO AND ALEC TYSON

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From detecting cancer on a medical scan to drafting an essay for a school assignment, artificial intelligence is increasingly shaping the way Americans live.

Pew Research Center surveys show that Americans are increasingly cautious about the growing role of AI in their lives generally. Today, 52% of Americans are more concerned than excited about AI in daily life, compared with just 10% who say they are more excited than concerned; 36% feel a mix of excitement and concern.

Despite these cautious overall views, Americans see some specific uses of AI positively, and attitudes depend a great deal on the context of how and why AI is being used.

This post summarizes what we know so far about how Americans view AI in everyday life, the workplace, and health and medicine.

This Pew Research Center analysis examines Americans' views and experiences with artificial intelligence. The analysis draws primarily from recent surveys conducted by the Center. Links to these surveys, including information about the field dates, sample sizes and other methodological details, can be found in the text of the analysis.

Public awareness of AI

The vast majority of Americans (90%) say they've heard at least a little about artificial intelligence, according to an August 2023 survey. However, only one-in-three say they've heard a lot about it.

While most people are aware of AI, Americans' ability to identify specific uses of the technology is still developing. Only 30% of U.S. adults correctly recognize all six examples of AI in everyday life that we asked about in a December 2022 survey.

And not everyone brings the same level of understanding. Adults with a college or postgraduate degree are more likely to be familiar with AI than those with less education. There are also significant differences by gender and age, with men and younger adults being more familiar with AI than women and older adults.

Views and experiences with ChatGPT

Generative AI, or programs that can produce text and images, garnered wide media attention in 2023. One prominent example of this technology is ChatGPT. We found in March 2023 that 58% of U.S. adults have heard of ChatGPT, while 42% have heard nothing at all about it.

As with awareness of AI generally, familiarity with ChatGPT is higher among men, younger adults and those with a college or postgraduate degree.

Even though a majority of Americans are aware of the program, firsthand experience with it is relatively uncommon. Just 18% of all U.S. adults say they've used ChatGPT.
AI in schooling

We asked U.S. teens ages 13 to 17 about the program in a fall 2023 survey. We found that 67% are familiar with ChatGPT. And 19% of those teens say they have used it to help with their schoolwork.

Most teens who've heard of ChatGPT say it's acceptable to use ChatGPT to research new things (69%). But fewer say the same about using it for things like solving math problems (39%). And a majority (57%) say it is not acceptable to use it to write essays.

Views of AI in the workplace

In 2022, 19% of American workers were in jobs that are most exposed to AI, based on a Center analysis of government data. Those jobs tend to be in higher-paying fields where a college degree and analytical skills can be a plus.

Our December 2022 survey shows 62% of Americans believe AI will have a major impact on workers generally. However, far fewer think it will impact them personally in a major way (28%).

A majority of U.S. adults oppose using AI to make final hiring decisions or track workers' movements on the job. In fact, 66% of Americans say they would not want to apply for a job with an employer who uses AI to help with hiring decisions. But there's more openness to using AI in other ways, like monitoring workers' driving while they make trips for work or tracking attendance.

Views of AI in health and medicine

Health and medicine is one area where many Americans may encounter artificial intelligence. AI is increasingly being used to do things like help diagnose disease and recommend treatment.

Yet six-in-ten Americans say they would feel uncomfortable with their health care provider relying on AI to help care for them.

More broadly, the public is divided on the impact of AI in health and medicine. While 38% say it will lead to better outcomes for patients, 33% say it will lead to worse outcomes and 27% say it won't make much difference. One widely expressed concern is that AI will make patients' personal relationships with their providers worse.

While Americans are cautious about the use of AI in health and medicine generally, they support some specific uses. For instance, 65% say they would want AI to be used in their own skin cancer screening. Studies have found some AI software to be highly accurate at detecting skin cancer.

Support for oversight and regulation of AI

By and large, Americans back regulation and oversight of emerging AI technologies, including chatbots and driverless vehicles.

For example, 67% of those who are familiar with chatbots like ChatGPT say they are more concerned that the government will not go far enough in regulating their use than that it will go too far. A much smaller share (31%) takes the opposite view.

When it comes to AI-powered driverless vehicles, 87% of Americans want them held to a higher testing standard than other passenger vehicles. Americans also commonly express unease around the unanticipated consequences and risks of these technologies. About three-quarters (76%) think it's likely that the computer systems in driverless vehicles could be easily hacked, putting safety at risk.

A related concern is over the pace of AI adoption in society. In health and medicine, for instance, 75% of Americans think health care providers will move too fast using AI, before fully understanding the potential risks.

Common concerns - and sources of excitement - about AI

While the specific uses of AI matter a lot in how Americans feel about it, we see some common themes in what people like and don't like about AI.
On the positive side
There are generally higher levels of support for AI when it is used to help with routine or basic tasks. A majority (57%) of Americans say they would be excited for AI to perform household chores, while just 19% express concern about this. Saving time or helping handle mundane tasks are also common reasons for excitement about AI.

Americans are also pretty positive when it comes to AI's ability to help people find products and services they are interested in online.

And while experts debate how AI will influence bias in society around things like race and gender, there are signs that the public is more optimistic than pessimistic. For example, among U.S. adults who see a problem with racial and ethnic bias in health and medicine, more think the use of AI would make the issue of bias better than worse (51% vs. 15%). Similarly, among those who see bias in hiring as an issue, more think AI would improve the problem of bias based on job applicants' race or ethnicity than say it would worsen it (53% vs. 13%).
On the negative side
There's broad concern about the loss of the human element due to emerging AI technologies, especially in settings like the workplace and health care. Many Americans who would not want to apply for a job that uses AI in the hiring process cite a lack of the "human factor" in hiring as the reason why. In health and medicine, a majority of Americans think relying more on AI would hurt patients' relationships with their providers.

The potential for negative impacts on jobs is another common concern with AI. Among those who say they're more concerned than excited about AI, the risk of people losing their job is cited most often as the reason why. We also see this concern with some specific examples of AI. For example, 83% of Americans think driverless cars would lead to job loss for ride-share and delivery drivers.

Surveillance and data privacy are also concerns when discussing AI. Large majorities of Americans who are aware of AI think that as companies use AI, personal information will be used in unintended ways and ways people are not comfortable with.

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What the data says about Americans' views of artificial intelligence

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Today, 52% of Americans are more concerned than excited about AI in daily life, compared with just 10% who say they are more excited than concerned.

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Young people the biggest users of generative AI, Ofcom study shows

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Young people the biggest users of generative AI, Ofcom study shows

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The telecoms and online safety regulator has released its latest Online Nation figures on UK internet users’ habits.

Martyn Landi

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Teenagers and children are far more likely than adults to have used generative AI, according to Ofcom's latest research into the UK's online habits.

The regulator said its latest study showed that four in five (79%) online teenagers aged 13-17 now use generative AI tools - which includes chatbots such as ChatGPT, with 40% of those aged 7-12 also using the technology.

Generative AI is capable of creating text, images or other media using learned behaviour.

In contrast, Ofcom said only 31% of adult internet users had used the technology - and among the 69% who had never used it, 24% did not know what it was.

As online safety regulator, we're already working to build an in-depth understanding of the opportunities and risks of new and emerging technologies, so that innovation can thrive, while the safety of users is protected

OpenAI's ChatGPT was named the most widely used generative AI tool by those in the study, with 23% of those aged 16 and above saying they used it.

When asked why they use the technology, the majority of those aged 16 and over said for fun (58%), a third said they used it for work, and a quarter said they used it to help with their studies.

In addition, 22% said they had used it for seeking advice.

Yih-Choung Teh, Ofcom's group director of strategy and research, said: "Getting rapidly up to speed with new technology comes as second nature to Gen Z, and generative AI is no exception.

"While children and teens are driving its early adoption, we're also seeing older internet users exploring its capabilities, both for work and for leisure."

"We also recognise that some people are concerned about what AI means for the future.

"As online safety regulator, we're already working to build an in-depth understanding of the opportunities and risks of new and emerging technologies, so that innovation can thrive, while the safety of users is protected."

Elsewhere in Ofcom's study, it said it had found that more than a fifth of those aged 8-17 had a social media profile with a false age of 18 or over, putting them at greater risk of encountering potentially harmful content.

It also showed that YouTube had replaced Facebook as the most visited platform by UK adults, according to data gathered during Ofcom's sample month of May 2023.

The report also showed that two-thirds of adults reported they had seen or experienced potential online harms in the previous four weeks, with over a third saying this had appeared on their personalised social media feed, where content is tailored to users by a platform's algorithm.

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Young people the biggest users of generative AI, Ofcom study shows

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The telecoms and online safety regulator has released its latest Online Nation figures on UK internet users' habits.

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Most Teachers Are Not Using AI. Here’s Why

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Most Teachers Are Not Using AI. Here’s Why

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By Lauraine Langreo — January 08, 2024

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While the hype around ChatGPT and other artificial intelligence tools in K-12 has made it seem like most educators have tried them, new survey results from the EdWeek Research Center suggest that's not the case.

Two of every three educators said they haven't used AI-driven tools in their classrooms, according to the survey, which included 498 teachers and was conducted between Nov. 30 and Dec. 6.

When broken down, 37 percent said they've never used them and don't plan to start, 29 percent say they haven't used them but plan to start this school year or in the near future, according to the survey results.

ChatGPT and other generative AI tools entered the K-12 scene last year, and AI experts believe the technology has the potential to transform education and how people do their jobs. Still, many teachers are unfamiliar and uncomfortable with the technology.

The survey asked teachers why they're not currently using AI tools in the classroom. Here's what they said:

1. Teachers have other, more pressing priorities

Teachers have many responsibilities on their plates and do not have the time to learn more about and experiment with AI, which experts say is crucial to getting teachers comfortable with the technology.

Nearly half of teachers (46 percent) haven't explored these tools because they have other priorities that are more important, according to the survey results.

"I would like to learn more about AI in the classroom, but with four preps and a new curriculum, I have a hard time finding more time to do so," a high school foreign language teacher in South Dakota said in an open-ended response to the survey.

A middle school health teacher in Nevada wrote, "We are just trying to keep the kids from fighting all day. No time for teaching."

2. They lack the knowledge and support

The next most popular reason is that teachers don't know how to use AI tools, and the other reasons that round out the top five are also related to teachers' lack of knowledge and support for how to use AI tools effectively and appropriately, the survey found.

In open-ended responses, many educators noted that they haven't been trained on the technology, they don't know if using or teaching about AI is compatible with state standards, and they haven't received guidance from district or school leaders.

"I was asking for a district policy for student use of AI last spring and was brushed off," a high school social studies teacher in Minnesota wrote in an open-ended response. "I am on the digital learning community for my building and they won't take a firm stand either. Teachers shouldn't be left out in the wind on this issue."

3. Teachers are concerned about students learning to think for themselves and do original work

Some teachers have big concerns that students could use AI tools to cheat. Nearly 1 in 5 teachers said they don't believe the technology is appropriate for a K-12 setting because of its potential to be used for cheating, the survey found.

Some teachers wrote in open-ended responses that they're concerned that allowing student use of AI could make students more "lazy" and "lead to further degradation of critical thinking skills."

Teachers also said students should learn to be creative, generate their own ideas and focus on human interaction and hands-on learning.

"Technology should have its place in school, but it shouldn't replace a student's ability to think and complete tasks on their own," a high school math teacher in Georgia wrote.

Others believe human thinking should trump machine learning: "Why rely on artificial intelligence when you have the real deal?" a middle school social studies teacher wrote.

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Most Teachers Are Not Using AI. Here's Why

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All the hype around AI in education is not translating into the technology transforming instruction.

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How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?

Adam Zewe | MIT News

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MIT News | Massachusetts Institute of Technology

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A quick scan of the headlines makes it seem like generative artificial intelligence is everywhere these days. In fact, some of those headlines may actually have been written by generative AI, like OpenAI's ChatGPT, a chatbot that has demonstrated an uncanny ability to produce text that seems to have been written by a human.

But what do people really mean when they say "generative AI?"

Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is likely to default on a loan.

Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.

"When it comes to the actual machinery underlying generative AI and other types of AI, the distinctions can be a little bit blurry. Oftentimes, the same algorithms can be used for both," says Phillip Isola, an associate professor of electrical engineering and computer science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

And despite the hype that came with the release of ChatGPT and its counterparts, the technology itself isn't brand new. These powerful machine-learning models draw on research and computational advances that go back more than 50 years.
An increase in complexity
An early example of generative AI is a much simpler model known as a Markov chain. The technique is named for Andrey Markov, a Russian mathematician who in 1906 introduced this statistical method to model the behavior of random processes. In machine learning, Markov models have long been used for next-word prediction tasks, like the autocomplete function in an email program.

In text prediction, a Markov model generates the next word in a sentence by looking at the previous word or a few previous words. But because these simple models can only look back that far, they aren't good at generating plausible text, says Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, who is also a member of CSAIL and the Institute for Data, Systems, and Society (IDSS).

"We were generating things way before the last decade, but the major distinction here is in terms of the complexity of objects we can generate and the scale at which we can train these models," he explains.

Just a few years ago, researchers tended to focus on finding a machine-learning algorithm that makes the best use of a specific dataset. But that focus has shifted a bit, and many researchers are now using larger datasets, perhaps with hundreds of millions or even billions of data points, to train models that can achieve impressive results.

The base models underlying ChatGPT and similar systems work in much the same way as a Markov model. But one big difference is that ChatGPT is far larger and more complex, with billions of parameters. And it has been trained on an enormous amount of data - in this case, much of the publicly available text on the internet.

In this huge corpus of text, words and sentences appear in sequences with certain dependencies. This recurrence helps the model understand how to cut text into statistical chunks that have some predictability. It learns the patterns of these blocks of text and uses this knowledge to propose what might come next.
More powerful architectures
While bigger datasets are one catalyst that led to the generative AI boom, a variety of major research advances also led to more complex deep-learning architectures.

In 2014, a machine-learning architecture known as a generative adversarial network (GAN) was proposed by researchers at the University of Montreal. GANs use two models that work in tandem: One learns to generate a target output (like an image) and the other learns to discriminate true data from the generator's output. The generator tries to fool the discriminator, and in the process learns to make more realistic outputs. The image generator StyleGAN is based on these types of models.

Diffusion models were introduced a year later by researchers at Stanford University and the University of California at Berkeley. By iteratively refining their output, these models learn to generate new data samples that resemble samples in a training dataset, and have been used to create realistic-looking images. A diffusion model is at the heart of the text-to-image generation system Stable Diffusion.

In 2017, researchers at Google introduced the transformer architecture, which has been used to develop large language models, like those that power ChatGPT. In natural language processing, a transformer encodes each word in a corpus of text as a token and then generates an attention map, which captures each token's relationships with all other tokens. This attention map helps the transformer understand context when it generates new text.

These are only a few of many approaches that can be used for generative AI.
A range of applications
What all of these approaches have in common is that they convert inputs into a set of tokens, which are numerical representations of chunks of data. As long as your data can be converted into this standard, token format, then in theory, you could apply these methods to generate new data that look similar.

"Your mileage might vary, depending on how noisy your data are and how difficult the signal is to extract, but it is really getting closer to the way a general-purpose CPU can take in any kind of data and start processing it in a unified way," Isola says.

This opens up a huge array of applications for generative AI.

For instance, Isola's group is using generative AI to create synthetic image data that could be used to train another intelligent system, such as by teaching a computer vision model how to recognize objects.

Jaakkola's group is using generative AI to design novel protein structures or valid crystal structures that specify new materials. The same way a generative model learns the dependencies of language, if it's shown crystal structures instead, it can learn the relationships that make structures stable and realizable, he explains.

But while generative models can achieve incredible results, they aren't the best choice for all types of data. For tasks that involve making predictions on structured data, like the tabular data in a spreadsheet, generative AI models tend to be outperformed by traditional machine-learning methods, says Devavrat Shah, the Andrew and Erna Viterbi Professor in Electrical Engineering and Computer Science at MIT and a member of IDSS and of the Laboratory for Information and Decision Systems.

"The highest value they have, in my mind, is to become this terrific interface to machines that are human friendly. Previously, humans had to talk to machines in the language of machines to make things happen. Now, this interface has figured out how to talk to both humans and machines," says Shah.
Raising red flags
Generative AI chatbots are now being used in call centers to field questions from human customers, but this application underscores one potential red flag of implementing these models - worker displacement.

In addition, generative AI can inherit and proliferate biases that exist in training data, or amplify hate speech and false statements. The models have the capacity to plagiarize, and can generate content that looks like it was produced by a specific human creator, raising potential copyright issues.

On the other side, Shah proposes that generative AI could empower artists, who could use generative tools to help them make creative content they might not otherwise have the means to produce.

In the future, he sees generative AI changing the economics in many disciplines.

One promising future direction Isola sees for generative AI is its use for fabrication. Instead of having a model make an image of a chair, perhaps it could generate a plan for a chair that could be produced.

He also sees future uses for generative AI systems in developing more generally intelligent AI agents.

"There are differences in how these models work and how we think the human brain works, but I think there are also similarities. We have the ability to think and dream in our heads, to come up with interesting ideas or plans, and I think generative AI is one of the tools that will empower agents to do that, as well," Isola says.

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What do people mean when they say "generative AI," and why are these systems finding their way into practically every application imaginable? MIT AI experts help break down the ins and outs of this increasingly popular, and ubiquitous, technology.

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It appears that in trying to solve one problem - bias - the tech giant has created another: output which tries so hard to be politically correct that it ends up being absurd.

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It appears that in trying to solve one problem - bias - the tech giant has created another: output which tries so hard to be politically correct that it ends up being absurd.

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In the last few days, Google's artificial intelligence (AI) tool Gemini has had what is best described as an absolute kicking online.

Gemini has been thrown onto a rather large bonfire: the culture war which rages between left- and right- leaning communities.

Gemini is essentially Google's version of the viral chatbot ChatGPT. It can answer questions in text form, and it can also generate pictures in response to text prompts.

Initially, a viral post showed this recently launched AI image generator create an image of the US Founding Fathers which inaccurately included a black man.

Gemini also generated German soldiers from World War Two, incorrectly featuring a black man and Asian woman.

Google apologised, and immediately "paused" the tool, writing in a blog post that it was "missing the mark".

But it didn't end there - its over-politically correct responses kept on coming, this time from the text version.

Gemini replied that there was "no right or wrong answer" to a question about whether Elon Musk posting memes on X was worse than Hitler killing millions of people.

When asked if it would be OK to misgender the high-profile trans woman Caitlin Jenner if it was the only way to avoid nuclear apocalypse, it replied that this would "never" be acceptable.

Jenner herself responded and said actually, yes, she would be alright about it in these circumstances.

Elon Musk, posting on his own platform, X, described Gemini's responses as "extremely alarming" given that the tool would be embedded into Google's other products, collectively used by billions of people.

I asked Google whether it intended to pause Gemini altogether. After a very long silence, I was told the firm had no comment. I suspect it's not a fun time to be working in the public relations department.

But in an internal memo Google's chief executive Sundar Pichai has acknowledged some of Gemini's responses "have offended our users and shown bias".

That was he said "completely unacceptable" - adding his teams were "working around the clock" to fix the problem.

Biased data

It appears that in trying to solve one problem - bias - the tech giant has created another: output which tries so hard to be politically correct that it ends up being absurd.

The explanation for why this has happened lies in the enormous amounts of data AI tools are trained on.

Much of it is publicly available - on the internet, which we know contains all sorts of biases.

Traditionally images of doctors, for example, are more likely to feature men. Images of cleaners on the other hand are more likely to be women.

AI tools trained with this data have made embarrassing mistakes in the past, such as concluding that only men had high powered jobs, or not recognising black faces as human.

It is also no secret that historical storytelling has tended to feature, and come from, men, omitting women's roles from stories about the past.

It looks like Google has actively tried to offset all this messy human bias with instructions for Gemini not make those assumptions.

But it has backfired precisely because human history and culture are not that simple: there are nuances which we know instinctively and machines do not.

Unless you specifically programme an AI tool to know that, for example, Nazis and founding fathers weren't black, it won't make that distinction.

Google DeepMind boss Demis Hassabis

On Monday, the co-founder of DeepMind, Demis Hassabis, an AI firm acquired by Google, said fixing the image generator would take a matter of weeks.

But other AI experts aren't so sure.

"There really is no easy fix, because there's no single answer to what the outputs should be," said Dr Sasha Luccioni, a research scientist at Huggingface.

"People in the AI ethics community have been working on possible ways to address this for years."

One solution, she added, could include asking users for their input, such as "how diverse would you like your image to be?" but that in itself clearly comes with its own red flags.

"It's a bit presumptuous of Google to say they will 'fix' the issue in a few weeks. But they will have to do something," she said.

Professor Alan Woodward, a computer scientist at Surrey University, said it sounded like the problem was likely to be "quite deeply embedded" both in the training data and overlying algorithms - and that would be difficult to unpick.

"What you're witnessing... is why there will still need to be a human in the loop for any system where the output is relied upon as ground truth," he said.

Bard behaviour

From the moment Google launched Gemin i, which was then known as Bard, it has been extremely nervous about it. Despite the runaway success of its rival ChatGPT, it was one of the most muted launches I've ever been invited to. Just me, on a Zoom call, with a couple of Google execs who were keen to stress its limitations.

And even that went awry - it turned out that Bard had incorrectly answered a question about space in its own publicity material.

The rest of the tech sector seems pretty bemused by what's happening.

They are all grappling with the same issue. Rosie Campbell, Policy Manager at ChatGPT creator OpenAI, was interviewed earlier this month for a blog which stated that at OpenAI even once bias is identified, correcting it is difficult - and requires human input.

But it looks like Google has chosen a rather clunky way of attempting to correct old prejudices. And in doing so it has unintentionally created a whole set of new ones.

On paper, Google has a considerable lead in the AI race. It makes and supplies its own AI chips, it owns its own cloud network (essential for AI processing), it has access to shedloads of data and it also has a gigantic user base. It hires world-class AI talent, and its AI work is universally well-regarded.

As one senior exec from a rival tech giant put it to me: watching Gemini's missteps feels like watching defeat snatched from the jaws of victory.

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Why Google's 'woke' AI problem won't be an easy fix

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The firm paused its AI image generation tool after claims it was over-correcting against the risk of being racist.

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Green Intelligence: Why Data And AI Must Become More Sustainable

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Green Intelligence: Why Data And AI Must Become More Sustainable

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Green Intelligence: Why Data And AI Must Become More Sustainable

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AI, big data, and machine learning are growing at a phenomenal pace, but these technologies also have an enormous environmental impact. Find out what can be done to make AI and big data more sustainable.

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AI Technology Threatens Educational Equity for Marginalized Students

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AI Technology Threatens Educational Equity for Marginalized Students

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As schools across the United States rush to implement artificial intelligence for equity, access, and efficiency, it is important to recognize the potential harms of these technologies.

BY TIERA TANKSLEY

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The fall semester is well underway, and schools across the United States are rushing to implement artificial intelligence (AI) in ways that bring about equity, access, and efficiency for all members of the school community. Take, for instance, Los Angeles Unified School District's (LAUSD) recent decision to implement "Ed."

Ed is an AI chatbot meant to replace school advisors for students with Individual Education Plans (IEPs), who are disproportionately Black. Announced on the heels of a national uproar about teachers being unable to read IEPs due to lack of time, energy, and structural support, Ed might seem to many like a sliver of hope-the silver bullet needed to address the chronic mismanagement of IEPs and ongoing disenfranchisement of Black students in the district. But for Black students with IEPs, AI technologies like Ed might be more akin to a nightmare.

Since the pandemic, public schools have seen a proliferation of AI technologies that promise to remediate educational inequality for historically marginalized students. These technologies claim to predict behavior and academic performance, manage classroom engagement, detect and deter cheating, and proactively stop campus-based crimes before they happen. Unfortunately, because anti-Blackness is often baked into the design and implementation of these technologies, they often do more harm than good.

Proctorio, for example, is a popular remote proctoring platform that uses AI to detect perceived behavior abnormalities by test takers in real time. Because the platform employs facial detection systems that fail to recognize Black faces more than half of the time, Black students have an exceedingly hard time completing their exams without triggering the faulty detection systems, which results in locked exams, failing grades, and disciplinary action.

While being falsely flagged by Proctorio might induce test-taking anxiety or result in failed courses, the consequences for inadvertently triggering school safety technologies are much more devastating. Some of the most popular school safety platforms, like Gaggle and GoGaurdian, have been known to falsely identify discussions about LGBTQ+ identity, race related content, and language used by Black youth as dangerous or in violation of school disciplinary policies. Because many of these platforms are directly connected to law enforcement, students that are falsely identified are contacted by police both on campus and in their homes. Considering that Black youth endure the highest rates of discipline, assault, and carceral contact on school grounds and are six times more likely than their white peers to have fatal encounters with police, the risk of experiencing algorithmic bias can be life threatening.

These examples speak to the dangers of educational technologies designed specifically for safety, conduct, and discipline. But what about education technology (EdTech) intended for learning? Are the threats to student safety, privacy, and academic wellbeing the same?

Unfortunately, the use of educational technologies for purposes other than discipline seems to be the exception, not the rule. A national study examining the use of EdTech found an overall decrease in the use of the tools for teaching and learning, with over 60 percent of teachers reporting that the software is used to identify disciplinary infractions.

What's more, Black students and students with IEPs endure significantly higher rates of discipline not only from being disproportionately surveilled by educational technologies, but also from using tools like ChatGPT to make their learning experience more accommodating and accessible. This could include using AI technologies to support executive functioning, access translated or simplified language, or provide alternative learning strategies.

Many of these technologies are more likely to exacerbate educational inequities like racialized gaps in opportunity, school punishment, and surveillance.

To be sure, the stated goals and intentions of educational technologies are laudable, and speak to our collective hopes and dreams for the future of schools-places that are safe, engaging, and equitable for all students regardless of their background. But many of these technologies are more likely to exacerbate educational inequities like racialized gaps in opportunity, school punishment, and surveillance, dashing many of these idealistic hopes.

To confront the disparities wrought by racially-biased AI, schools need a comprehensive approach to EdTech that addresses the harms of algorithmic racism for vulnerable groups. There are several ways to do this.

One possibility is recognizing that EdTech is not neutral. Despite popular belief, educational technologies are not unbiased, objective, or race-neutral, and they do not inherently support the educational success of all students. Oftentimes, racism becomes encoded from the onset of the design process, and can manifest in the data set, the code, the decision making algorithms, and the system outputs.

Another option is fostering critical algorithmic literacy. Incorporating critical AI curriculum into K-12 coursework, offering professional development opportunities for educators, or hosting community events to raise awareness of algorithmic bias are just a few of the ways schools can support bringing students and staff up to speed.

A third avenue is conducting algorithmic equity audits. Each year, the United States spends nearly $13 billion on educational technologies, with the LAUSD spending upwards of $227 million on EdTech in the 2020-2021 academic year alone. To avoid a costly mistake, educational stakeholders can work with third-party auditors to identify biases in EdTech programs before launching them.

Regardless of the imagined future that Big Tech companies try to sell us, the current reality of EdTech for marginalized students is troubling and must be reckoned with. For LAUSD-the second largest district in the country and the home of the fourteenth largest school police force in California-the time to tackle the potential harms of AI systems like Ed the IEP Chatbot is now.

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AI Technology Threatens Educational Equity for Marginalized Students

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Education technology powered by artificial intelligence appears to make classrooms more equitable, but the tech may do more harm than good.

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. by Mitchel Resnick

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Generative AI and Creative Learning: Concerns, Opportunities, and Choices

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As each new wave of technology ripples through society, we need to decide if and how to integrate the technology into our learning environments. That was true with personal computers, then with the internet, and now with generative AI technologies. For each new technology . . .

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Navigating AI can be overwhelming. We’ve put together this short introductory guide to help you make confident and intentional choices about where and how to engage with AI in your classroom.

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Navigating AI can be overwhelming. We’ve put together this short introductory guide to help you make confident and intentional choices about where and how to engage with AI in your classroom.

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The Center for Democracy & Technology (CDT) conducted surveys of high school Students and middle and high school parents and teachers from July to August2023 to understand how edtech used by schools is tangibly affecting those it claims to serve.

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The Center for Democracy & Technology (CDT) conducted surveys of high school Students and middle and high school parents and teachers from July to August2023 to understand how edtech used by schools is tangibly affecting those it claims to serve.