6.390 Introduction to Machine Learning (Formerly 6.036)
Authors: Leslie Kaelbling, Serena Booth, Marion Boulicault, Dheekshita Kumar, Rodrigo Ochigame, Tess Smidt
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; classification, regression, reinforcement-learning, sequence learning, clustering; classical and neural-network methods.
The course has weekly labs, in which students work in pairs and have an opportunity to discuss their work with an instructor during a check-off process. Each weekly lab has an accompanying SERC question and discussion prompt. These SERC questions aim to help the students connect the technical content of the class to the social consequences of seemingly-technical design decisions.
Weekly Labs: 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
- 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
6.031 Software Construction
Author: Rob Miller, Abby Jaques
Lecture Module: “Moral Lenses Case Study”
Keywords: Software Construction
Module Goals: A reading and class activity to explore the implications of a proposed change to change the ranking algorithm for posts on a social media site, and examine:
- What are the main benefits it will or may provide, and to whom?
- What are the main harms it will or may cause, and to whom?
- How could you maximize the benefits and minimize the harms, and ensure that they are distributed fairly?
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.
Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study, by Haoran Zhang, Thomas Hartvigsen, and Marzyeh Ghassemi (MIT)
Keywords: algorithmic fairness, deep learning, medical imaging, machine learning for health care
The Right to Be an Exception to a Data-Driven Rule, by Sarah H. Cen and Manish Raghavan (MIT)
Keywords: data-driven decision-making, rights and duties, individualization, uncertainty, harm
Twitter Gamifies the Conversation, by C. Thi Nguyen (University of Utah), Meica Magnani (Northeastern University), and Susan Kennedy (Santa Clara University)
Keywords: social media, social epistemology, Twitter, gamification, value capture, technology ethics
Patenting Bias: Algorithmic Race and Ethnicity Classifications, Proprietary Rights, and Public Data, by Tiffany Nichols (Harvard University)
Keywords: racial and ethnic classifications, algorithmic bias, patents, public data
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
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