Algorithms and AI for a better world Myth news

Among the benefits offered by algorithmic decision making and artificial intelligence-in this revolutionizing speed, performance and predictive abilities in a wide range of fields-manish Raghavan works on limiting the risks related to this, and also seeking the possibilities of using technology to help in existing social fears.

“Ultimately, I want my research to strive for better solutions for long -term social problems,” says Raghavan, a professor of career development at Houston, who is a joint member of the department between the mit Sloan School of Management and the myth of Schwarzman College of Computing at the Department of Electrical Engineering and Computer Science, as well decision -making).

A good example of Raghavan's intention can be found in his exploration AI in employment.

Raghavan says: “It is difficult to argue that historically employment practices were particularly good or worth behavior, and the tools that learn on the basis of historical data inherit all prejudices and mistakes that people have made in the past.”

Here, however, Raghavan cites a potential opportunity.

“It has always been difficult to measure discrimination,” he says, adding: “AI -based systems are sometimes easier to observe and measure than people, and one of the goals of my work is to understand how we can use this better visibility to come up with new ways of determining when the systems behave badly.”

Growing up in the region of the bay of San Francisco with his parents who have IT steps, Raghavan says that he originally wanted to become a doctor. However, just before starting his studies, his love for mathematics and computers called him to follow his family in computer science. After spending summer as a student at the University of Cornell with Jon Kleinberg, a professor of computer science and computer science, he decided that he wanted to get his doctorate there by writing his thesis on “social influence on making decisions.”

Raghavan won awards for his work, including the National Science Foundation Research Fellowships Award, Award of Dr. Microsoft Research Fellowship and the Award of the Department of PhD students of IT Cornell University.

In 2022 he joined the MIT Faculty.

Perhaps the medicine dealing with early interest, Raghavan conducted research or finding a very accurate tool for algorithmic screening used in the divergence of patients with gastrointestinal bleeding, known as Glasgow-Blatchford Pank (GBS), are improved using complementary doctors.

“GBS is approximately as good as people, but this does not mean that there are no individual patients or small groups of patients in which GBS is wrong and doctors may be right,” he says. “We hope that we can identify these patients in advance so that doctors' feedback is particularly valuable there.”

Raghavan also worked on how online platforms affect their users, taking into account how social media algorithms observe the content that the user will choose, and then show them more of the same type of content. Raghavan claims that the difficulty is that users can choose what they perceive in the same way as they can grab a bag from potato chips, which are of course delicious, but not so nutritious. Experience can be satisfactory at the moment, but it can make the user feel a bit sick.

Raghavan and his colleagues have developed a model of how a user with arguments-on the purpose of immediate satisfaction compared to the wishes of long-term satisfaction-comes into interaction with the platform. The model shows how you can change the platform design to encourage more healthy experience. The model won an example of the Modeling Track Award at the Machinery Computing Conference conference on economics and calculations.

“Long -term satisfaction is ultimately important, even if everything you care about is the company's interests,” says Raghavan. “If we can start building evidence that the interests of users and corporate are more adapted, I hope that we can press on healthier platforms without having to solve conflicts of interests between users and platforms. Of course, this is idealistic. But I have the impression that enough people in these companies think that there is a place to make all happy, and they simply lack concept and technical tools, so that this will happen.”

As for his process of inventing ideas for such tools and concepts, how to best use computing techniques, Raghavan says that his best ideas come to him when he was thinking about the problem for some time. He would advise his students to follow the example of putting a very difficult problem for one day, and then returning to him.

“The next day is often better,” he says.

When no problem or teaches, Raghavan can often be found outside on the football field as a coach of the Harvard men's football club, which is cultivating.

“I can't delay if I know that I will have to spend the evening on the pitch, and this gives me something I can wait for at the end of the day,” he says. “I try to have things in my schedule that seems to me at least as important to me as work to put these challenges and failures in context.”

Because Raghavan is wondering how to use computing technologies to best serve our world, he says that he thinks that the most exciting thing that is happening in his field is the idea that AI will open a new look at “people and human society.”

“I hope that” says, “that we can use it to better understand ourselves.”