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Many Top AI Researchers Get Financial Backing From Big Tech

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As a grad student working on artificial intelligence, Mohamed Abdalla could probably walk into a number of well-paid industry jobs. Instead, he wants to draw attention to how Big Tech’s big bucks may be warping the perspective of his field.

Abdalla, who is finishing his PhD at the University of Toronto, has coauthored a paper highlighting the number of top AI researchers—including those who study the ethical challenges raised by the technology—who receive funding from tech companies. That can be a particular problem, he says, when corporate AI systems raise ethical issues, such as algorithmic bias, military use, or questions about the fairness and accuracy of face recognition programs.

Abdalla found that more than half of tenure-track AI faculty at four prominent universities who disclose their funding sources have received some sort of backing from Big Tech. Abdalla says he doesn’t believe any of those faculty are acting unethically, but he thinks their funding could bias their work—even unconsciously. He suggests universities introduce rules to raise awareness of potential conflicts of interest.

Industry funding for academic research is nothing new, of course. The flow of capital, ideas, and people between companies and universities is part of a vibrant innovation ecosystem. But large tech companies now wield unprecedented power, and the importance of cutting-edge AI algorithms to their businesses has led them to tap academia for talent.

Students with AI expertise can command large salaries at tech firms, but companies also back important research and young researchers with grants and fellowships. Many top AI professors have been lured away to tech companies or work part-time at those companies. Besides money, large companies can offer computational resources and data sets that most universities cannot match.

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The growing reach and power of Big Tech prompted Abdalla to question how it influences his field in more subtle ways.

Together with his brother, also a graduate student, Abdalla looked at how many AI researchers at Stanford, MIT, UC Berkeley, and the University of Toronto have received funding from Big Tech over their careers.

The Abdallas examined the CVs of 135 computer science faculty who work on AI at the four schools, looking for indications that the researcher had received funding from one or more tech companies. For 52 of those, they couldn’t make a determination. Of the remaining 83 faculty, they found that 48, or 58 percent, had received funding such as a grant or a fellowship from one of 14 large technology companies: Alphabet, Amazon, Facebook, Microsoft, Apple, Nvidia, Intel, IBM, Huawei, Samsung, Uber, Alibaba, Element AI, or OpenAI. Among a smaller group of faculty that works on AI ethics, they also found that 58 percent of those had been funded by Big Tech. When any source of funding was included, including dual appointments, internships, and sabbaticals, 32 out of 33, or 97 percent, had financial ties to tech companies. “There are very few people that don't have some sort of connection to Big Tech,” Abdalla says.

Adballa says industry funding is not necessarily compromising, but he worries that it might have some influence, perhaps discouraging researchers from pursuing certain projects or prompting them to agree with solutions proposed by tech companies. Provocatively, the Abdallas’ paper draws parallels between Big Tech funding for AI research and the way tobacco companies paid for research into the health effects of smoking in the 1950s.

“I think that the vast majority of researchers are unaware,” he says. “They are not actively seeking to push one agenda or the other.”

Others in the field of AI are concerned by the influence of industry money. At the year’s biggest gathering of AI researchers, a new workshop, called Resistance In AI, will look at how AI “has been concentrating power in the hands of governments and companies and away from marginalized communities.”

But ties to industry are pervasive and often found across groups that examine ethical uses of AI. For instance, two out of the three cochairs of the Fairness, Accountability and Transparency conference, a prominent event that looks at the societal impact of AI, work for Alphabet subsidiaries.

Kristian Lum, a statistician at the nonprofit Human Rights Data Analysis Group, who is on the conference’s executive committee, says the conference, like other events, receives corporate sponsorships. But she says the conference’s policies state that the sponsors do not have control over the content or speakers. Lum says those involved with the conference are careful to disclose potential conflicts of interest.


“Big Tech does have a lot of power,” says Lum, whose employer is funded by foundations. “I do think it’s something that people are increasingly aware of.”

Others say the issue is more complicated.

Meredith Whittaker, a research scientist at NYU, previously worked for Google on a project that connected the company with academic research. She also led protests inside the company in 2018 against its policies on sexual misconduct and surveillance.

“People know who pays them,” she says. But she says it’s unfair to assume that someone funded by a company cannot be critical of Big Tech. She says several researchers who work at tech companies are critical of their employer’s technology. And she says pushback within companies can help check their power. “Worker organizing and worker dissent are only increasing as the status of this technology becomes more and more apparent,” she says.

A spokesperson for Google says the company’s policies prohibit staff from seeking to influence academic work. “Google’s collaborations with academic and research institutions are not driven by policy influence in any way,” the spokesperson says. “We are a huge supporter of academic research because it allows us to work with academics who are looking to solve the same problems that we are.”

Ben Recht, a professor at UC Berkeley, has previously criticized the idea of researchers simultaneously working for a university and a company. But he doesn’t think corporate funding for AI should be seen as inherently bad. “You can make a capitalist argument that it is good for companies to pursue ethical technology,” he says. “I think that this is something that many of them strive to do.”

Recht also points out that even without industry funding, academics can produce ethically questionable work, like the algorithms that underpin face recognition or those that help turn social media platforms into echo chambers and sources of misinformation. And Recht also notes that the money that flows from government agencies, including the military, can also influence the direction of research.

For his part, Abdalla worries that drawing attention to the ties between Big Tech and academia might affect his prospects of getting a job, because professors are often expected to help bring in funding. “I was told not to push this,” he says.

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