AI News, Building AI systems that make fair decisions

Building AI systems that make fair decisions

A growing body of research has demonstrated that algorithms and other types of software can be discriminatory, yet the vague nature of these tools makes it difficult to implement specific regulations.

Suresh studies the societal implications of automated systems in MIT Professor John Guttag’s Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports.

I learned how computational tools could profoundly affect biology and medicine, since humans can’t process massive amounts of data in the way that machines can.

Towards the end of my undergrad, I started doing research with [professor of computer science and engineering] Peter Szolovits, who focuses on utilizing big medical data and machine learning to come up with new insights.

I stayed to get my master’s degree in computer science, and now I’m in my first year as a PhD student studying personalized medicine and societal implications of machine learning.

A: When I ask for help, whether it's related to a technical detail, a high-level problem, or general life advice, people are genuinely willing to lend support, discuss problems, and find solutions, even if it takes a long time.

A: When we think about machine learning problems with real-world applications, and the goal of eventually getting our work in the hands of real people, there’s a lot of existing legal, ethical, and philosophical considerations that arise.

Along those lines, I painted a full-wall mural in my room a while ago, I frequently spend hours at MIT's pottery studio, and I love making up recipes and taking photos.

Things that seem like huge consequences at the time, like taking an extra class or graduating slightly later, aren't actually an issue when the time rolls around, and a lot of people do it.

I think machine learning gets a reputation of being a very difficult, expert-only endeavor, which scares people away and creates a pretty homogenous group of “experts.” I

Building AI systems that make fair decisions

A growing body of research has demonstrated that algorithms and other types of software can be discriminatory, yet the vague nature of these tools makes it difficult to implement specific regulations.

Suresh studies the societal implications of automated systems in MIT Professor John Guttag’s Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports.

I learned how computational tools could profoundly affect biology and medicine, since humans can’t process massive amounts of data in the way that machines can.

Towards the end of my undergrad, I started doing research with [professor of computer science and engineering] Peter Szolovits, who focuses on utilizing big medical data and machine learning to come up with new insights.

I stayed to get my master’s degree in computer science, and now I’m in my first year as a PhD student studying personalized medicine and societal implications of machine learning.

A: When I ask for help, whether it's related to a technical detail, a high-level problem, or general life advice, people are genuinely willing to lend support, discuss problems, and find solutions, even if it takes a long time.

A: When we think about machine learning problems with real-world applications, and the goal of eventually getting our work in the hands of real people, there’s a lot of existing legal, ethical, and philosophical considerations that arise.

Along those lines, I painted a full-wall mural in my room a while ago, I frequently spend hours at MIT's pottery studio, and I love making up recipes and taking photos.

Things that seem like huge consequences at the time, like taking an extra class or graduating slightly later, aren't actually an issue when the time rolls around, and a lot of people do it.

I think machine learning gets a reputation of being a very difficult, expert-only endeavor, which scares people away and creates a pretty homogenous group of “experts.” I

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