In an era where algorithmic decision-making and artificial intelligence (AI) are transforming speed, efficiency, and predictive abilities across various sectors, Manish Raghavan is dedicated to addressing the risks associated with these advancements. He also aims to harness these technologies to tackle enduring societal challenges.
As the Drew Houston Career Development Professor, Raghavan is a prominent member of both the MIT Sloan School of Management and the MIT Schwarzman College of Computing, within the Department of Electrical Engineering and Computer Science. Additionally, he serves as a principal investigator at the Laboratory for Information and Decision Systems (LIDS).
A prime illustration of Raghavan’s mission is his investigation into AI applications in the hiring process. “Historically, hiring practices have not been particularly effective or just,” he notes. “Tools that learn from past data often inherit the biases and errors that humans have made.” Despite this challenge, he sees a significant opportunity through AI.
Raghavan states, “AI-driven systems can sometimes be easier to analyze and understand than human behaviors. My goal is to leverage this enhanced visibility to identify when these systems may be performing inadequately.”
Hailing from Silicon Valley, where both of his parents studied computer science, Raghavan initially aspired to become a doctor. However, his passion for mathematics and computing led him toward a career in computer science. After a transformative summer research stint at Cornell University with Jon Kleinberg, he decided to pursue his PhD there, focusing on “The Societal Impacts of Algorithmic Decision-Making.”
His research has garnered numerous accolades, including the National Science Foundation Graduate Research Fellowship, a Microsoft Research PhD Fellowship, and the Cornell University Department of Computer Science PhD Dissertation Award. He joined the MIT faculty in 2022.
Raghavan’s work even touches on healthcare, studying a highly accurate algorithmic screening tool known as the Glasgow-Blatchford Score (GBS), which is used for triaging patients with gastrointestinal bleeding. He investigates whether the accuracy of this tool can be improved with guidance from expert physicians.
“The GBS performs on par with human averages, but that doesn’t mean it’s infallible,” Raghavan asserts. “Our objective is to pinpoint patients where expert feedback could significantly enhance decision-making.”
Additionally, Raghavan analyses the influence of online platforms on user behavior. He observes that social media algorithms often amplify the types of content users engage with, akin to indulging in a bag of chips — immediately gratifying yet ultimately unsatisfying. “We’ve developed a model that explores the tension between short-term pleasure and long-term satisfaction.” His work in this area won the Exemplary Applied Modeling Track Paper Award at the 2022 Association for Computing Machinery Conference on Economics and Computation.
“Long-term satisfaction is essential, even from a corporate perspective,” he asserts. “If we can show that user and corporate interests can align, we might advocate for healthier online environments without needing to address fundamental conflicts of interest.” While he considers this optimistic, Raghavan is encouraged by the belief within tech companies that improvement is possible.
Raghavan’s creative process often thrives on taking a step back. “I often find that allowing some time to reflect on a challenging problem leads to clearer insights,” he advises his students, recommending they give themselves a day to think it over before returning to it.
Outside of academia, Raghavan indulges in his love for soccer, coaching the Harvard Men’s Soccer Club. “Having to be at practice helps me stay on top of my responsibilities, and it adds meaning to my day,” he shares enthusiastically.
As Raghavan reflects on integrating computational technologies to benefit society, he expresses excitement about how AI can uncover new understanding of humanity and social behavior. “I’m optimistic that we can harness AI to deepen our understanding of ourselves,” he concludes.
Photo credit & article inspired by: Massachusetts Institute of Technology
