Among the benefits that algorithmic decision-making and artificial intelligence offer – including revolutionizing speed, efficiency and predictive ability across a wide range of domains – Manish Raghavan is working to mitigate the associated risks while seeking opportunities to apply technology to facilitate solve pre-existing problems. social concerns.
“Ultimately, I want my research to move toward better solutions to long-standing societal problems,” says Raghavan, the Drew Houston Career Development Professor, who is a faculty member of the MIT Sloan School of Management and the MIT Schwarzman College of Computing in the Department of Electrical Engineering and Computer Science and head of the Systems Laboratory Information and Decision-Making Systems (LIDS).
A good example of Raghavan’s intentions can be found in his research on the apply of artificial intelligence in the hiring process.
Raghavan says: “It’s hard to argue that past hiring practices were particularly good or worth preserving, and that tools that learn from historical data inherit all the biases and mistakes that people have made in the past.”
Here, however, Raghavan brings up a potential opportunity.
“Discrimination has always been difficult to measure,” he says, adding: “AI-based systems are sometimes easier to observe and measure than humans, and one of the goals of my work is to understand how we can use this greater visibility to develop new ways of checking when systems behave badly.”
Growing up in the San Francisco Bay Area with parents who both have degrees in computer science, Raghavan says he initially wanted to be a doctor. However, just before starting his studies, his love for mathematics and computer science prompted him to follow his family’s example and pursue computer science. After spending his undergraduate summer doing research at Cornell University under Jon Kleinberg, professor of computer science and computer science, he decided he wanted to pursue a PhD there, writing his thesis on “The Social Impact of Algorithmic Decision Making.”
Raghavan has won awards for his work, including a National Science Foundation Graduate Research Fellowship Award, a Microsoft Research PhD Fellowship, and a Doctoral Dissertation Award from the Department of Computer Science at Cornell University.
He joined the MIT faculty in 2022.
Perhaps echoing his early interests in medicine, Raghavan conducted research on whether scores on a highly true algorithmic screening tool used to triage patients with gastrointestinal bleeding, known as the Glasgow-Blatchford Score (GBS), improve with complementary expert physician advice .
“The GBS test is about as good as it gets in humans, but that doesn’t mean there aren’t individual patients or small groups of patients where GBS is wrong and the doctors are probably right,” he says. “We hope that we can identify these patients in advance, which will make their doctors’ opinions especially valuable.”
Raghavan also worked on the impact of online platforms on users, considering how social media algorithms observe the content a user selects and then serve them more of the same type of content. The difficulty, says Raghavan, is that users can choose what they watch in the same way they might pick up a bag of potato chips, which are delicious, of course, but not very nutritious. The experience may be satisfying in the moment, but it may leave the user feeling slightly ill.
Raghavan and his colleagues developed a model of how a user with conflicting desires – immediate gratification versus a desire for long-term gratification – interacts with the platform. The model shows how the platform design can be changed to provide a healthier experience. The model won the Exemplary Applied Modeling Track Paper Award at the 2022 Association for Computing Machinery Conference on Economics and Computation.
“At the end of the day, long-term satisfaction is important, even if you only care about the company,” says Raghavan. “If we can start to gather evidence that user and corporate interests are more aligned, I hope we can promote healthier platforms without having to resolve conflicts of interest between users and platforms. Of course, this is idealism. But I feel like enough people in these companies believe there is room to make everyone happy and they just lack the conceptual and technical tools to make it happen.”
When it comes to the process of coming up with ideas for such tools and concepts for how to best apply computational techniques, Raghavan says the best ideas come to him after he has thought about the problem for some time. He says he advises his students to follow his example and set aside a very challenging problem for a day and then come back to it.
“Everything is often better the next day,” he says.
When he’s not solving problems or teaching, Raghavan can often be found on the soccer field coaching the Harvard Men’s Soccer Club, a position he greatly appreciates.
“I can’t delay knowing I’ll have to spend the evening in the field, and it gives me something to look forward to at the end of the day,” he says. “I try to have things on my schedule that seem at least as important to me as work to put these challenges and setbacks in context.”
As Raghavan considers how to apply computational technologies to best serve our world, he states that the most electrifying thing about his field is the idea that artificial intelligence will enable fresh insights into “humans and human society.”
“I hope,” he says, “that this will help us understand ourselves better.”