6.170 Software Studio
Authors: Daniel Jackson, Arvind Satyanarayan, Serena Booth
Resources: 5 assignments with SERC design reflections; module lecture on responsible design; final project
Keywords: software design, local government, ethical assessment
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
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: 4 weekly labs, each with a SERC question and discussion prompt
Keywords: machine learning; bias and fairness in machine learning; data bias; model bias
6.031 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?
Considered Design Cards
Authors: Mikaela Springsteen, Madhurima Das, Swati Gupta
The activities which these cards contain are designed to subvert expectations and elicit conversation regarding the social and ethical responsibilities associated with design. They are organized into five categories: inputs, processes, outputs, feedback loops, and ecosystems, each of which focuses on a different part of the design or product lifecycle.
These cards are intended to help foster in users a creative and flexible approach to the potential impacts of a design process, rather than treating any such outcomes or impacts as a formulaic “check-box” aspect of design. Indeed, flexibility is built into the very nature of the cards themselves—these cards can be used in a variety of settings, and while the cards may have specific time and material requirements listed, they can often be adapted and ‘remixed’ to suit specific learning environments and contexts.
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. Some cases are paired with active learning projects developed by students at MIT and reviewed by faculty and senior researchers.
Summer 2024
Complete Delete: In Practice, Clicking “Delete” Rarely Deletes. Should it?, by Simson Garfinkel
Keywords: crypto shredding, cryptographic erasure, data governance, law enforcement, mass storage, operating systems, privacy, remnant data, sexting, system design, usability
Winter 2024
Integrals and Integrity: Generative AI Tries to Learn Cosmology, by Bruce A. Bassett
Keywords: artificial intelligence, AI agents, generative AI, cosmology, dark energy
AI’s Regimes of Representation: A Community-Centered Study of Text-to-Image Models in South Asia, by Rida Qadri, Renee Shelby, Cynthia L. Bennett, and Remi Denton
Keywords: human-centered AI, AI harms, text-to-image models, generative AI, non-Western AI fairness, South Asia
Winter 2023
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
Summer 2022
“Porsche Girl”: When a Dead Body Becomes a Meme, by Nadia de Vries (University of Amsterdam)
Keywords: digital death, bodies, memes, online abuse, Nikki Castouras
Privacy and Paternalism: The Ethics of Student Data Collection, by Kathleen Creel (Northeastern University) and Tara Dixit (Chantilly High School, Virginia)
Keywords: user data privacy, student data, contextual integrity, educational technology, children’s rights, surveillance
Winter 2022
Protections for Human Subjects in Research: Old Models, New Needs?, by Laura Stark (Vanderbilt University)
Keywords: human-subjects research, informed consent, institutional review boards, big data
Summer 2021
Hacking Technology, Hacking Communities: Codes of Conduct and Community Standards in Open Source, by Christina Dunbar-Hester (University of Southern California)
Keywords: open source software, diversity and inclusion, community governance, gender, race, values in computing, codes of conduct
Identity, Advertising, and Algorithmic Targeting: Or How (Not) to Target Your “Ideal User”, by Tanya Kant (University of Sussex)
Keywords: targeting, advertising, algorithms, identity, profiling
Winter 2021
Who Collects the Data? A Tale of Three Maps, by Catherine D’Ignazio (MIT) and Lauren Klein (Emory University)
Keywords: redlining, social inequality and oppression, missing data, counterdata, matrix of domination, who questions
The Case of the Nosy Neighbors, by Johanna Gunawan and Woodrow Hartzog (Northeastern University)
Keywords: user data privacy, technology in norm enforcement, facial recognition, mass surveillance, mass scraping of public data
Active Learning Projects Developed at MIT
Active Learning Project: Exploring the Functionalities, Data and Interfaces of a Modern Online Advertising System (PDF - 1.1MB) (DOCX - 3.2MB)
An exercise to explore the ethical implications of digital advertising, grounded in the functionalities, data, and interfaces driving ad systems in the modern era. This lab focuses on Facebook’s Ads Manager.
- Associated case study: Kant, T. (2021). Identity, Advertising, and Algorithmic Targeting: Or How (Not) to Target Your “Ideal User.” MIT Case Studies in Social and Ethical Responsibilities of Computing, (Summer 2021). https://doi.org/10.21428/2c646de5.929a7db6
Active Learning Project: Active Learning Project on Developing Codes of Conduct (PDF) (DOCX)
An exercise in developing a code of conduct for a team-based course in Github-hosted project repositories.
- Associated case study: Dunbar-Hester, C. (2021). Hacking Technology, Hacking Communities: Codes of Conduct and Community Standards in Open Source. MIT Case Studies in Social and Ethical Responsibilities of Computing, Summer 2021. https://doi.org/10.21428/2c646de5.07bc6308
