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Do algorithmic hiring systems reduce bias? Manish Raghavan & Solon Barocas explore the issue in a new entry to Brookings's "AI and Bias" series. brook.gs/33XK9a4

💭❓[Thread:] How can the field of computing contribute to social change, without tacitly assuming that tomorrow's social structures will basically resemble the past? ⚡️ arxiv.org/abs/1912.04883

I am *really* excited about our new paper, Roles for Computing in Social Change, on its way to Former Account of the ACM FAccT/FAT* Conference: arxiv.org/abs/1912.04883 This paper comes from *years* of thinking and talking among ourselves (me + @red_abebe Solon Barocas Manish Raghavan David Robinson @[email protected] Jon Kleinberg) ...

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I love this thoughtful piece by David Robinson @[email protected] on how algorithms tell stories about who we are, and how we might use them as lenses into others’ humanity. oreilly.com/radar/reclaimi…

In our latest ML podcast, @MelissaKoide talks to Manish Raghavan of Harvard CRCS, Deborah Hellman of UVA Law School, and Stephen Hayes of Relman Colfax PLLC about how to measure the fairness of AI/ML models & the implications for effective fair lending oversight. #FRLPods finreglab.org/podcasts

My secret wish for our new paper (w/ Jon Kleinberg & Manish Raghavan): that we had titled it “My relationship with Doritos (Bonus: may explain your love/hate of social media)” 🧵to explain what connects Doritos, machine learning, and user data. bit.ly/39ZEZn3 1/N

Got the chance to talk at the Simons Institute's AI & Humanity Workshop last week! Presented two ongoing works with Andrew Ilyas Aleksander Madry Manish Raghavan on building trust in AI & the governance of data-driven algorithms. Check out the video here youtube.com/watch?v=OpFY9D…

This week at the SIGecom seminar series we have Christopher Harshaw: "Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model" & Manish Raghavan: "The Challenge of Understanding What Users Want" Check out sigecom.org/seminar for the link to join!

Applications for the Machine Learning in Economics Summer Institute (MLESI) 2023 are open! Great opp. for grad students to learn about cutting-edge research, generate new ideas, and build community at intersection of econ, ML and CS. chicagobooth.edu/research/cente…

The application deadline for MLESI is in one week (Mar 17)! Apply now! We have an exciting lineup of participating faculty including (on twitter) Susan Athey, @peterbergman_, Peter Hull, Sanjog Misra, Emma Pierson, Manish Raghavan, Chenhao Tan and many others!

Is AI the new HR? Our panel with Manish Raghavan AlexandraReeveGivens Hilke Schellmann starts in an hour at #sxsw. Are we going to let AI become a high tech pathway to discrimination? #ai schedule.sxsw.com/2023/events/PP…

Are you doing a ecoomics-related PhD? Interested in AI/ML? Then apply to our Summer Institute in Machine Learning! Learn from awesome faculty Meet other like-minded students Get exposed to cool, new ideas bit.ly/3ZRvTxr (march 24 deadline)

Interested in some combination of formal privacy, generative models, fairness & causal inference? We're looking for researcher to join a team at MIT IDE collaborating with MIT-IBM Watson AI Lab in this area.

chris will be presenting this at the web conference next week, so here's the promised slightly longer thread on this paper 1/n

a pleasant surprise this morning: we won the best paper award at this year's The Web Conference

I’m excited to share our work on Auditing for Human Expertise! We develop a statistical test to assess whether human forecasters exercise expert judgment or otherwise incorporate information which is not available to *any* predictive algorithm (1/n) arxiv.org/abs/2306.01646

Many papers study the accuracy-diversity trade-off in recommendations. In new work, we show that this "trade-off" is an artifact of mismeasuring accuracy and ignoring the *consumption constraints* of users. w/ Emma Pierson, Manish Raghavan, Jon Kleinberg, and Nikhil Garg Thread⬇️

I'll be at the ICLR DMLR and PMLDS workshops presenting ongoing work with Divya Shanmugam (+ a wonderful team including Manish Raghavan, MIT Clinical and Applied Machine Learning, Bonnie Berger Lab, Emma Pierson)! Details in thread below 🧵 [1/4]