Governing Algorithms and Algorithmic Governance

Course Numbers: INST878D/CMSC839C

This cross-cutting interdisciplinary course, taught jointly between the College of Information Studies and the Department of Computer Science, investigates the role that algorithms and automated decision-making systems play in markets, societies, and policymaking and the technologies we have to address their unintended consequences. The course connects policy and computational conceptualizations of transparency, security, fairness, privacy, manipulation, and accountability through a series of case studies and burning debates. Students will collaborate cross-disciplinary and be encouraged to work through difficult trade-offs to reach consensus. By discussing recent applications of algorithms for social and consumer sorting, and the moderation and generation of content, all in the context of the technologies designed to address algorithmic harms, students will engage with the pressing challenges and opportunities in the governance of and by algorithms.

This course counts as "qualifying" for CS PhD students within the AI area.

Instructors

Ido Sivan-Sevilla
Assistant Professor
College of Information Studies
Email: sevilla (at) umd (dot) edu

Gabriel Kaptchuk
Assistant Professor
Department of Computer Science
Email: kaptchuk (at) umd (dot) edu

Kanav Gupta (Teaching Assistant)
Department of Computer Science
Email: kanav (at) umd (dot) edu

Course Information

Syllabus and Grading: Available here.
Meeting Times: Tuesdays, 11:00am–1:45pm
Course Structure: 1 Hour lecture, 1 hour interative activity or discussion, 1 hour project work time
Course website: ga-ag.com
Course Project: Available Here

Class Slides

[Class 1] [Class 2] [Class 3] [Class 4] [Class 5] [Class 6] [Class 7] [Class 8] [Class 9] [Class 10] [Class 11] [Class 12]

Reading List

Class 1

What does introducing a technology do?
The politics of artifacts
“Unintended” consequences:

Class 2

How does regulation work?"
Technology and regulation from an STS perspective
On Power
Optional Additional Readings

Class 3

What is machine learning (socially)
Case Study

Class 4

What is implicit in the reality machine learning is trying to create?
Hazards in a AI world
Secret labor of machine learning

Class 5

Class 6

Ongoing regulatory efforts
The limits of regulatory approaches
Optional Additional Readings

Class 7

Optional Additional Readings

Class 8

Balancing Privacy and Data Use in the Census
The resulting controversy

Class 9

Introduction to PETs
Policy-relevant uses of PETs

Class 10

GDPR

Class 11

Assisting & Improving The Regulatory Process:
Critique:

Class 12

Note that the response question is different this week:

For each case, we are asking you to think like a reviewer. You don’t have to answer all of the questions below, as different questions may be more relevant to different cases. Instead, we hope that for each case you can holistically respond to the proposal with whatever thoughts you think are most important to apprising the proposal. Some example questions you might want to consider:

Cases:
A critical vision:

Class 13

No reading