Amid the benefits that algorithmic decision-making and artificial intelligence offer, including revolutionary speed, efficiency and predictive capabilities in a wide range of fields, Manish Raghavan is working to mitigate the associated risks, while also seeking opportunities to apply technologies to help with pre-existing problems. social concerns.
“Ultimately, I want my research to advance better solutions to long-standing social problems,” says Raghavan, the Drew Houston Career Development Professor, a faculty member shared between the MIT Sloan School of Management and the MIT Schwarzman College of Computing. in the United States. Department of Electrical and Computer Engineering, as well as principal investigator of the Laboratory of Information and Decision Systems (LIDS).
A good example of Raghavan's intent can be found in his exploration of the use of ai in recruiting.
Raghavan says, “It's hard to argue that hiring practices have historically been particularly good or worth preserving, and tools that learn from historical data inherit all the biases and errors that humans have made in the past.”
Here, however, Raghavan cites a potential opportunity.
“It has always been difficult to measure discrimination,” he says, adding: “Sometimes ai-powered systems are easier to observe and measure than humans, and one of the goals of my work is to understand how we can “Leverage this improved visibility to come up with new ways to discover when systems are misbehaving.”
Raghavan grew up in the San Francisco Bay area with parents who have computer science degrees and says he originally wanted to be a doctor. However, just before starting college, his love of mathematics and computer science called him to follow his family's example in computer science. After spending a summer as a student doing research at Cornell University with Jon Kleinberg, a professor of computer and information sciences, he decided he wanted to earn his PhD there and wrote his thesis on “The Social Impacts of Algorithmic Decision Making.” .
Raghavan won awards for his work, including a National Science Foundation Graduate Research Fellowship Program award, a Microsoft PhD Research Fellowship, and the University of Computer Science Department's PhD Dissertation Award. Cornell.
In 2022 he joined the MIT faculty.
Perhaps recalling his early interest in medicine, Raghavan has investigated whether the determinations of a highly accurate algorithmic screening tool used in classifying patients with gastrointestinal bleeding, known as the Glasgow-Blatchford Score (GBS), improve with the help of experts complementary. medical advice.
“GBS is about as good as humans on average, but that doesn't mean there aren't individual patients, or small groups of patients, where GBS is wrong and doctors are probably right,” he says. “Our hope is to be able to identify these patients early so that doctors' opinions are especially valuable there.”
Raghavan has also worked on how online platforms affect their users, considering how social media algorithms look at the content a user chooses and then show them more of the same type of content. The difficulty, Raghavan says, is that users can choose what they see in the same way they would pick up a bag of chips, which of course are delicious but not as nutritious. The experience may be satisfying in the moment, but it can make the user feel a little sick.
Raghavan and his colleagues have developed a model of how a user with conflicting desires (for immediate gratification versus a desire for long-term satisfaction) interacts with a platform. The model demonstrates how the design of a platform can be changed to encourage a healthier experience. The model won the Exemplary Applied Modeling Track Paper award at the 2022 Association for Computing Machinery Computing and Economics Conference.
“Long-term satisfaction is ultimately important, even if all you care about is the company's interests,” says Raghavan. “If we can start to generate evidence that the interests of users and companies are more aligned, my hope is that we can drive healthier platforms without needing to resolve conflicts of interest between users and platforms. Of course, this is idealistic. But my sense is that enough people in these companies believe there is room to make everyone happier, and they simply lack the conceptual and technical tools to make it happen.”
As for his process of generating ideas for such tools and concepts for how to best apply computational techniques, Raghavan says his best ideas come to him when he has been thinking about a problem off and on for a while. He would advise his students, he says, to follow his example of putting aside a very difficult problem for a day and then coming back to it.
“Things are often better the next day,” he says.
When he's not solving a problem or teaching, Raghavan can often be found outdoors on a soccer field, coaching the Harvard Men's Soccer Club, a position he appreciates.
“I can't procrastinate if I know I'll have to spend the night in the country and that gives me something to look forward to at the end of the day,” he says. “I try to have things on my schedule that feel at least as important as work to put those challenges and setbacks in context.”
As Raghavan considers how to apply computing technologies to better serve our world, he says the most exciting thing happening in his field is the idea that ai will open up new insights about “humans and human society.”
“I hope,” he says, “that we can use it to better understand ourselves.”
