Amid the advantages that algorithmic decision-making and synthetic intelligence supply — together with revolutionizing pace, effectivity, and predictive potential in a huge vary of fields — Manish Raghavan is working to mitigate related dangers, whereas additionally searching for alternatives to use the applied sciences to assist with preexisting social issues.
“I ultimately want my research to push towards better solutions to long-standing societal problems,” says Raghavan, the Drew Houston Career 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).
instance 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 worth preserving, and tools that learn from historical data inherit all of the biases and mistakes that humans have made in the past.”
Here, nevertheless, Raghavan cites a potential alternative.
“It’s always been hard to measure discrimination,” he says, including, “AI-driven systems are sometimes easier to observe and measure than humans, and one goal of my work is to understand how we might leverage this improved visibility to come up with new ways to figure out when systems are behaving badly.”
Growing up within the San Francisco Bay Area with dad and mom who each have pc science levels, Raghavan says he initially wished to be a physician. Just earlier than beginning school, although, his love of math and computing referred to as him to observe his household instance into pc science. After spending a summer time as an undergraduate doing analysis at Cornell University with Jon Kleinberg, professor of pc science and data science, he determined he wished to earn his PhD there, writing his thesis on “The Societal Impacts of Algorithmic Decision-Making.”
Raghavan gained awards for his work, together with 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 school.
Perhaps hearkening again to his early curiosity in medication, Raghavan has completed analysis on whether or not the determinations of a extremely correct algorithmic screening software utilized in triage of sufferers with gastrointestinal bleeding, referred to as the Glasgow-Blatchford Score (GBS), are improved with complementary professional doctor recommendation.
“The GBS is roughly 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 wrong and doctors are likely to be right,” he says. “Our hope is that we can identify these patients ahead of time so that doctors’ feedback is particularly valuable there.”
Raghavan has additionally labored on how on-line platforms have an effect on their customers, contemplating how social media algorithms observe the content material a consumer chooses and then present them extra of that very same type of content material. The problem, Raghavan says, is that customers could also be selecting what they view in the identical method they could seize bag of potato chips, that are in fact scrumptious however not all that nutritious. The expertise could also be satisfying within the second, however it may well go away the consumer feeling barely sick.
Raghavan and his colleagues have developed a mannequin of how a consumer with conflicting wishes — for fast gratification versus a want of longer-term satisfaction — interacts with a platform. The mannequin demonstrates how a platform’s design might be modified to encourage a extra healthful expertise. The mannequin gained the Exemplary Applied Modeling Track Paper Award on the 2022 Association for Computing Machinery Conference on Economics and Computation.
“Long-term satisfaction is ultimately important, even if all you care about is a company’s interests,” Raghavan says. “If we can start to build evidence that user and corporate interests are more aligned, my hope is that we can push for 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 at these companies believe there’s room to make everyone happier, and they just lack the conceptual and technical tools to make it happen.”
Regarding his means of developing with concepts for such instruments and ideas for how one can finest apply computational strategies, Raghavan says his finest concepts come to him when he’s been fascinated about a drawback off and on for a time. He would advise his college students, he says, to observe his instance of placing a very tough drawback away for a day and then coming again to it.
“Things are often better the next day,” he says.
When he is not puzzling out a drawback or educating, Raghavan can usually be discovered open air on a soccer subject, as a coach of the Harvard Men’s Soccer Club, a place he cherishes.
“I can’t procrastinate if I know I’ll have to spend the evening at the field, and it gives me something to look forward to at the end of the day,” he says. “I try to have things in my schedule that seem at least as important to me as work to put those challenges and setbacks into context.”
As Raghavan considers how one can apply computational applied sciences to finest serve our world, he says he finds probably the most thrilling factor happening his subject is the concept AI will open up new insights into “humans and human society.”
“I’m hoping,” he says, “that we can use it to better understand ourselves.”