6.036 Introduction to Machine Learning
Authors: Leslie Kaelbling, Serena Booth, Marion Boulicault, Dheekshita Kumar, Rodrigo Ochigame
Weekly Labs: 4 weekly labs, each with a SERC question and discussion prompt
Keywords: machine learning; bias and fairness in machine learning; data bias; model bias
6.864 Quantitative Methods for Natural Language Processing
Authors: Jacob Andreas, Catherine D'Ignazio, Harini Suresh
Assignment: "Dataset Creation"
Keywords: data annotation; natural language processing; machine learning; content moderation
Topics addressed:
- Critical assessment of how and by whom a given dataset was created
- What its limitations might be
- What the data should and should not be used for
MIT Case Studies in Social and Ethical Responsibilities of Computing
Brief, specially commissioned and peer-reviewed cases intended to be effective for undergraduate instruction across a range of classes and fields of study.
Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle, by Harini Suresh and John Guttag
Keywords: fairness in machine learning, societal implications of machine learning, algorithmic bias, AI ethics
Differential Privacy and the 2020 US Census, by Simson Garfinkel (George Washington University)
Keywords: differential privacy, disclosure avoidance, statistical disclosure limitation, US Census Bureau
Algorithmic Redistricting and Black Representation in US Elections, by Zachary Schutzman (MIT)
Keywords: redistricting, algorithms, race, politics, elections