Privacy and Surveillance

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?

STS.012/STS.008 Science in Action: Technologies and Controversies in Everyday Life

Author: Dwai Banerjee

Lecture Module: “Big Data and Personal Privacy”

Keywords: privacy; AI; surveillance; data ethics

Module Goals:

  • To find overlaps and differences in the experience of students learning about their right to privacy
  • To discuss whether they believe existing rights to be adequate
  • To examine whether current ethical standards (such as those instituted by the GDPR) sufficiently protect their rights (as they exist or as they believe should exist).

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

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.

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

Public Debate on Facial Recognition Technologies in China, by Tristan G. Brown, Alexander Statman, and Celine Sui

Keywords: facial recognition, Chinese law, social media

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

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

Differential Privacy and the 2020 US Census, by Simson Garfinkel (George Washington University)

Keywords: differential privacy, disclosure avoidance, statistical disclosure limitation, US Census Bureau

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

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

Course Info

Learning Resource Types

notes Lecture Notes
co_present Instructor Insights
assignment Multiple Assignment Types