Given the advantages that algorithmic decision-making and artificial intelligence offer – including revolutionizing speed, efficiency and predictive ability in a big selection of areas – Manish Raghavan is working to mitigate the risks related to it, while taking a look at ways the technologies are already available to support it existing risks to handle social concerns.
“Ultimately, I would like my research to advance higher solutions to long-standing societal problems,” says Raghavan, Drew Houston Career Development Professor within the Sloan School of Management and the Department of Electrical Engineering and Computer Science at MIT and a senior researcher within the Information and Decision Systems Laboratory ( LIDS).
An excellent example of Raghavan's intent is his exploration of the usage of AI in hiring.
Raghavan says, “It's hard to argue that hiring practices previously were particularly good or price preserving, and that tools that learn from historical data inherit all of the biases and mistakes people made previously.”
However, here Raghavan mentions a possible opportunity.
“It has all the time been difficult to measure discrimination,” he says, adding: “AI-driven systems are sometimes easier to look at and measure than humans, and a goal of my work is to know how we will achieve this improved visibility “We can use it to develop something.” New ways to seek out out when systems are behaving badly.”
Raghavan grew up within the San Francisco Bay Area with parents who each had computer science degrees. Raghavan says he originally desired to change into a physician. But just before he began college, his love of math and computer science led him to follow his family's lead into computer science. After a summer of research as an undergraduate at Cornell University with Jon Kleinberg, Professor of Computer and Information Science, he decided to pursue his doctorate there and wrote his dissertation on “The Social Impact of Algorithmic Decision Making.”
Raghavan has 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.
Raghavan can have drawn on his early interest in medicine and investigated whether the determinations of a highly accurate algorithmic screening tool for triaging patients with gastrointestinal bleeding, often called the Glasgow-Blatchford Score (GBS), might be improved by complementary experts medical advice.
“GBS is about nearly as good as the typical person, but that doesn't mean there aren't individual patients or small groups of patients where GBS is unsuitable and doctors are probably right,” he says. “We hope that we will discover these patients early in order that the feedback from doctors there may be particularly precious.”
Raghavan has also worked on how online platforms impact their users by studying how social media algorithms observe the content a user selects after which show them more content of the identical type. The difficulty, says Raghavan, is that users may select what they need to look at in the identical way as in the event that they were grabbing a bag of potato chips, that are after all delicious but not particularly nutritious. The experience could also be satisfactory in the mean time, but may leave the user feeling barely nauseous.
Raghavan and his colleagues have developed a model that shows how a user with conflicting desires – for immediate gratification and the need for longer-term gratification – interacts with a platform. The model shows how the design of a platform may be modified to advertise a healthier 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 necessary, even if you happen to only have the interests of an organization at heart,” says Raghavan. “If we will start to assemble evidence that user and company interests are higher aligned, I hope we will advocate for healthier platforms without having to resolve conflicts of interest between users and platforms.” This is, after all, idealistic. But I get the impression that enough people in these firms consider that there may be room to make everyone happier and that they simply lack the conceptual and technical tools to attain it.”
Speaking about his strategy of coming up with ideas for such tools and ideas for the optimal application of computing techniques, Raghavan says that his best ideas come to him after he has considered an issue on and off for some time. He says he would advise his students to follow his example and postpone a really difficult problem for a day after which get back to it.
“It’s often higher the following day,” he says.
When he's not solving an issue or teaching, Raghavan can often be found outside on a soccer field coaching the Harvard Men's Soccer Club, a position he values.
“I can't hesitate once I know I even have to spend the evening on the sector, and it gives me something to stay up for at the top of the day,” he says. “I attempt to have things in my schedule that appear not less than as necessary to me because the work of putting these challenges and setbacks into context.”
As Raghavan thinks about how we will use computing technologies to best serve our world, he says he finds the concept that AI will open up latest insights into “humans and human society” most enjoyable in his field.
“I hope,” he says, “that we will use it to know ourselves higher.”
