Amid the advantages that algorithmic decision-making and artificial intelligence offer — including revolutionizing speed, efficiency, and predictive ability in an enormous range of fields — Manish Raghavan is working to mitigate associated risks, while also in search of opportunities to use the technologies to assist with preexisting social concerns.
“I ultimately want my research to push towards higher solutions to long-standing societal problems,” says Raghavan, the Drew Houston Profession Development Professor in MIT’s Sloan School of Management and the Department of Electrical Engineering and Computer Science and a principal investigator on the Laboratory for Information and Decision Systems (LIDS).
example of Raghavan’s intention might be present in his exploration of the use AI in hiring.
Raghavan says, “It’s hard to argue that hiring practices historically have been particularly good or price preserving, and tools that learn from historical data inherit all the biases and mistakes that humans have made up to now.”
Here, nonetheless, Raghavan cites a possible opportunity.
“It’s at all times been hard to measure discrimination,” he says, adding, “AI-driven systems are sometimes easier to look at and measure than humans, and one goal of my work is to grasp how we would leverage this improved visibility to give you latest ways to determine when systems are behaving badly.”
Growing up within the San Francisco Bay Area with parents who each have computer science degrees, Raghavan says he originally desired to be a health care provider. Just before starting college, though, his love of math and computing called him to follow his family example into computer science. After spending a summer as an undergraduate doing research at Cornell University with Jon Kleinberg, professor of computer science and data science, he decided he desired to earn his PhD there, writing his thesis on “The Societal Impacts of Algorithmic Decision-Making.”
Raghavan won awards for his work, including a National Science Foundation Graduate Research Fellowships Program award, a Microsoft Research PhD Fellowship, and the Cornell University Department of Computer Science PhD Dissertation Award.
In 2022, he joined the MIT faculty.
Perhaps hearkening back to his early interest in medicine, Raghavan has done research on whether the determinations of a highly accurate algorithmic screening tool utilized in triage of patients with gastrointestinal bleeding, often known as the Glasgow-Blatchford Rating (GBS), are improved with complementary expert physician advice.
“The GBS is roughly pretty much as good as humans on average, but that doesn’t mean that there aren’t individual patients, or small groups of patients, where the GBS is improper and doctors are more likely to be right,” he says. “Our hope is that we will discover these patients ahead of time in order that doctors’ feedback is especially priceless there.”
Raghavan has also worked on how online platforms affect their users, considering how social media algorithms observe the content a user chooses after which show them more of that very same type of content. The problem, Raghavan says, is that users could also be selecting what they view in the identical way they may grab bag of potato chips, that are after all delicious but not all that nutritious. The experience could also be satisfying within the moment, but it may well leave the user feeling barely sick.
Raghavan and his colleagues have developed a model of how a user with conflicting desires — for immediate gratification versus a wish of longer-term satisfaction — interacts with a platform. The model demonstrates how a platform’s design might be modified to encourage a more healthful experience. The model won the Exemplary Applied Modeling Track Paper Award on the 2022 Association for Computing Machinery Conference on Economics and Computation.
“Long-term satisfaction is ultimately vital, even when all you care about is an organization’s interests,” Raghavan says. “If we will start to construct evidence that user and company interests are more aligned, my hope is that we will push for healthier platforms while not having to resolve conflicts of interest between users and platforms. In fact, that is idealistic. But my sense is that enough people at these firms consider there’s room to make everyone happier, they usually just lack the conceptual and technical tools to make it occur.”
Regarding his means of coming up with ideas for such tools and ideas for the best way to best apply computational techniques, Raghavan says his best ideas come to him when he’s been fascinated by an issue on and off for a time. He would advise his students, he says, to follow his example of putting a really difficult problem away for a day after which coming back to it.
“Things are sometimes higher the following day,” he says.
When he isn’t puzzling out an issue or teaching, Raghavan can often be found outdoors on a soccer field, as a coach of the Harvard Men’s Soccer Club, a position he cherishes.
“I can’t procrastinate if I do know I’ll should spend the evening at the sector, and it gives me something to look ahead to at the tip of the day,” he says. “I attempt to have things in my schedule that appear a minimum of as vital to me as work to place those challenges and setbacks into context.”
As Raghavan considers the best way to apply computational technologies to best serve our world, he says he finds essentially the most exciting thing occurring his field is the concept that AI will open up latest insights into “humans and human society.”
“I’m hoping,” he says, “that we will use it to higher understand ourselves.”
