6.390 Introduction to Machine Learning (Formerly 6.036) (https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-responsibilities-of-computing-serc/pages/privacy-surveillance/introduction-to-machine-learning/) 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 (https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-responsibilities-of-computing-serc/pages/ai-algorithms/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 6.031 Software Construction (https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-responsibilities-of-computing-serc/pages/6031-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. Summer 2023 Pretrial Risk Assessment on the Ground: Algorithms, Judgments, Meaning, and Policy (https://mit-serc.pubpub.org/pub/czviu6qc?readingCollection=e057132a), by Cristopher Moore,Elise Ferguson, and Paul Guerin Keywords: risk assessment, pretrial detention, algorithmic fairness, criminal justice reform Winter 2023 Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study (https://mit-serc.pubpub.org/pub/algorithmic-chest/), 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 (https://mit-serc.pubpub.org/pub/right-to-be-exception/), by Sarah H. Cen and Manish Raghavan (MIT) Keywords: data-driven decision-making, rights and duties, individualization, uncertainty, harm Twitter Gamifies the Conversation (https://mit-serc.pubpub.org/pub/twitter-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 Summer 2022 Patenting Bias: Algorithmic Race and Ethnicity Classifications, Proprietary Rights, and Public Data (https://mit-serc.pubpub.org/pub/patenting-bias/), by Tiffany Nichols (Harvard University) Keywords: racial and ethnic classifications, algorithmic bias, patents, public data Winter 2022 Differential Privacy and the 2020 US Census (https://mit-serc.pubpub.org/pub/differential-privacy-2020-us-census/release/1), 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 (https://mit-serc.pubpub.org/pub/algorithmic-redistricting-in-us-elections/release/1), by Zachary Schutzman (MIT) Keywords: redistricting, algorithms, race, politics, elections Summer 2021 Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle (https://mit-serc.pubpub.org/pub/potential-sources-of-harm-throughout-the-machine-learning-life-cycle/release/2), by Harini Suresh and John Guttag Keywords: fairness in machine learning, societal implications of machine learning, algorithmic bias, AI ethics