AI and Algorithms

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

Course Info

Learning Resource Types

notes Lecture Notes
co_present Instructor Insights
assignment Multiple Assignment Types