RES.TLL-008 | Spring 2023 | Non Credit

Social and Ethical Responsibilities of Computing (SERC)

Inequality, Justice, & Human Rights

STS.047 Quantifying People: A History of Social Science

Author: Will Deringer

Lecture module: “Quantify and Punish: Data, Race, and Policing from the Burgess Method to Big Data”

Keywords: ​​policing; criminal justice; race; racism; actuarial techniques; risk assessments; big data; surveillance

Questions addressed:

  • What role have quantitative data, computational methods, and social science played in the construction of modern systems of criminal justice?
  • How has quantification contributed to the injustices of modern policing and punishment—to the creation and maintenance of a system that disproportionately and unjustly targets, punishes, incarcerates, and kills people of color, especially Black citizens?
  • What can history tell us about the role that data and computation should—or should not—play in efforts to create a more just system of justice in the future?

17.806 Quantitative Research Methods IV: Advanced Topics

Author: In Song Kim

Lecture Module: “Analyzing the Impact of Police Stopping in Political Behavior” 

Keywords: policing, stop-question-and-frisk, racial minorities, political behavior 

Module Goals: This problem set explores how/whether policing against citizens and against racial minorities affects political behavior by leveraging a variety of data sources available online, including micro-level administrative data on policing.                    

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.

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

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

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

Algorithmic Redistricting and Black Representation in US Elections, by Zachary Schutzman (MIT)

Keywords: redistricting, algorithms, race, politics, elections

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

Winter 2021

The Dangers of Risk Prediction in the Criminal Justice System, by Julia Dressel (Dartmouth College) and Hany Farid (University of California, Berkeley)

Keywords: algorithmic risk prediction, algorithmic bias, algorithmic fairness, algorithmic transparency, criminal justice

The Bias in the Machine: Facial Recognition Technology and Racial Disparities, by Sidney Perkowitz (Emory University)

Keywords: facial recognition, justice system, racial equity, false arrest

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

Active Learning Projects Developed at MIT

Active Learning Project: Active Learning Project on Developing Codes on 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

Course Info

As Taught In
Spring 2023
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Multiple Assignment Types