6.883 Data-Driven Decision Making and Society — Spring 2021

Instructors: Aleksander Mądry, Asuman Ozdaglar and Shibani Santurkar
TA: Kai Xiao
Teaching staff email: 6883-team@lists.csail.mit.edu
Time and place: MW 2:30-4 pm on Zoom (you will need an MIT Zoom account to access this meeting—email us if you don't have such account)
Units: 3-0-9 AAGS
Prerequisites
Mathematical background at the level of 6.042/18.062 or equivalent, machine learning background at the level of 6.036 or equivalent.

Course description

The last decade brought us tremendous advances in the power and sophistication of the data-driven decision-making techniques that are at our disposal. Encouraged by this progress, we are witnessing a broad deployment of these techniques in the real world. They now touch on—and sometime even govern—just about every aspect of our lives.

However, as much as these techniques were deployed with the promise of bringing a decisively positive change, it has become abundantly clear that they often are a mixed blessing, at best. Indeed, it turns out that the interface of algorithmic decision-making and society is rife with subtle and non-obvious interactions, undesirable feedback loops, and unintended consequences.

How should we make sense of and navigate these issues?

The goal of this class is to survey some of the key challenges emerging in the context of societal impact of data-driven decision making as well as to create a forum where the students can discuss potential approaches to addressing these challenges.

This class will be a mix of background lectures (given by the instructors and guest lecturers) and student-led discussion sessions. These discussions will involve the leading student team selecting (in consultation with the instruction staff) 1-2 papers to constitute the core discussion material for that session. They will also be in charge of preparing a set of discussion-seeding questions, preparing a short (5-10 min) framing presentation to kick off the session as well as preparing a brief (1-2 pages) report summarizing the discussion afterwards.

All the students would be expected to read the papers to be discussed (or their specified parts) and submit a brief, paragraph-long answer to a (pre-defined) question ahead of time. Finally, each student will be asked to write a short (3-4 pages) essay on one of the discussed topics or a theme that cuts across a number of topics.

Grade will comprise leading the class discussion (as a part of the team) [50%], involvement in class discussion (as a participant) [20%], and final-project essay [30%].



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