'It feels like a gold rush," says Timnit Gebru. "In fact, it is a gold rush. And a lot of the people who are making money are not the people actually in the midst of it. But it's humans who decide whether all this should be done or not. We should remember that we have the agency to do that." Gebru is talking about her specialised field: artificial intelligence. On the day we speak via a video call, she is in Kigali, Rwanda, preparing to host a workshop and chair a panel at an international conference on AI. It will address the huge growth in AI's capabilities, as well as something that the frenzied conversation about AI misses out: the fact that many of its systems may well be built on a huge mess of biases, inequalities and imbalances of power. This gathering, the clunkily titled International Conference on Learning Representations, marks the first time people in the field have come together in an African country – which makes a powerful point about big tech's neglect of the global south. When Gebru talks about the way that AI "impacts people all over the world and they don't get to have a say on how they should shape it", the issue is thrown into even sharper relief by her backstory. In her teens, Gebru was a refugee from the war between Ethiopia, where she grew up, and Eritrea, where her parents were born. After a year in Ireland, she made it to the outskirts of Boston, Massachusetts, and from there to Stanford University in northern California, which opened the way to a career at the cutting edge of the computing industry: Apple, then Microsoft, followed by Google. But in late 2020, her work at Google came to a sudden end. As the co-leader of Google's small ethical AI team, Gebru was one of the authors of an academic paper that warned about the kind of AI that is increasingly built into our lives, taking internet searches and user recommendations to apparently new levels of sophistication and threatening to master such human talents as writing, composing music and analysing images. The clear danger, the paper said, is that such supposed "intelligence" is based on huge data sets that "overrepresent hegemonic viewpoints and encode biases potentially damaging to marginalised populations". Put more bluntly, AI threatens to deepen the dominance of a way of thinking that is white, male, comparatively affluent and focused on the US and Europe. In response, senior managers at Google demanded that Gebru either withdraw the paper, or take her name and those of her colleagues off it. This triggered a run of events that led to her departure. Google says she resigned; Gebru insists that she was fired. What all this told her, she says, is that big tech is consumed by a drive to develop AI and "you don't want someone like me who's going to get in your way.

These narratives distract from what we call the "pathologies of scale" that become more entrenched every day: large-scale AI models are still largely controlled by Big Tech firms because of the enormous computing and data resources they require, and also present well-documented concerns around discrimination, privacy and security vulnerabilities, and negative environmental impacts. Large-scale AI models like Large Language Models (LLMs) have received the most hype, and fear-mongering, over the past year. "Opinion You Can Have the Blue Pill or the Red Pill, and We're Out of Blue Pills." Greg Noone, "'Foundation models' may be the future of AI. They're also deeply flawed," Tech Monitor, November 11, 2021 (updated February 9, 2023); Dan McQuillan, "We Come to Bury ChatGPT, Not to Praise It," danmcquillan.org,

Then-Google AI Research Scientist Timnit Gebru speaks during the TechCrunch Disrupt SF 2018 conference. Last Thursday POLITICO's Mark Scott, author of the Digital Bridge newsletter, interviewed the computer scientist and activist Timnit Gebru about a recent open letter from her Distributed AI Research Institute that argued -- contra the Future of Life Institute's high-profile letter calling for an "AI pause" -- that the major harms caused by AI are already here, and therefore "Regulatory efforts should focus on transparency, accountability and preventing exploitative labor practices." Mark asked her what she thinks regulators' role should be in this fast-moving landscape, and how society might take a more proactive approach to shaping AI before it simply shapes us. This conversation has been edited for length and clarity. Why is it important to increase the transparency and accountability for how AI systems are deployed, and how would it benefit people's understanding of how the technology works?

As a leading researcher on the ethics of artificial intelligence, Timnit Gebru has long believed that machine-learning algorithms could one day power much of our lives. What she didn't predict was just how quickly this would happen. "I didn't imagine people would be like, 'Let's replace lawyers with a chatbot,' or'Let's sell AI generated art that looks exactly like someone else's,'" she says over video from her home in California's Bay Area. "I didn't anticipate using chatbots in search engines, which is a bonkers idea that everyone is now racing to do." Dr. Gebru, 39, is the founder and executive director of the Distributed Artificial Intelligence Research Institute (DAIR), a nonprofit she launched in 2021 with backing from the MacArthur Foundation, the Rockefeller Foundation, the Ford Foundation and others.

As a leading researcher on the ethics of artificial intelligence, Timnit Gebru has long believed that machine-learning algorithms could one day power much of our lives. What she didn't predict was just how quickly this would happen. "I didn't imagine people would be like, 'Let's replace lawyers with a chatbot,' or'Let's sell AI generated art that looks exactly like someone else's,'" she says over video from her home in California's Bay Area. "I didn't anticipate using chatbots in search engines, which is a bonkers idea that everyone is now racing to do to." Ms. Gebru, 39, is the founder and executive director of the Distributed Artificial Intelligence Research Institute (DAIR), a nonprofit she launched in 2021 with backing from the MacArthur Foundation, the Rockefeller Foundation, the Ford Foundation and others.

The prominent computer scientist discusses the quest for ethical artificial intelligence.

Suspended Google AI researcher Blake Lemoine told Fox's Tucker Carlson that the system is a'child' that could'escape control' of humans. Lemoine, 41, who was put on administrative leave earlier this month for sharing confidential information, also noted that it has the potential to do'bad things,' much like any child. 'Any child has the potential to grow up and be a bad person and do bad things. That's the thing I really wanna drive home,' he told the Fox host. 'It's been alive for maybe a year -- and that's if my perceptions of it are accurate.'

The way Timnit Gebru sees it, the foundations of the future are being built now. In Silicon Valley, home to the world's biggest tech companies, the artificial intelligence (AI) revolution is already well under way. Software is being written and algorithms are being trained that will determine the shape of our lives for decades or even centuries to come. If the tech billionaires get their way, the world will run on artificial intelligence. Cars will drive themselves and computers will diagnose and cure diseases. Art, music and movies will be automatically generated.

It takes courage to speak truth to the most powerful technology companies in the world. Timnit Gebru is a truth teller. Gebru was the most senior Black woman to lead a team of AI ethicists at Google, hired to find issues and improve the technology. She was ultimately fired after co-authoring a paper that did just that; it exposed racial discrimination and environmental harm in large-scale artificial intelligence systems at the company. Her ousting sparked protests by scholars and Google employees around the world.

The world of AI research is in shambles. From the academics prioritizing easy-to-monetize schemes over breaking novel ground, to the Silicon Valley elite using the threat of job loss to encourage corporate-friendly hypotheses, the system is a broken mess. And Google deserves a lion's share of the blame. There were approximately 85,000 research papers published globally on the subject of AI/ML in the year 2000. Fast-forward to 2021 and there were nearly twice as many published in the US alone. Our speakers and sessions give "must-watch" a whole new meaning!