CS 565

Course Description

This course introduces critical social analysis of the ethical aspects of Artificial Intelligence. Students will learn about the theories of ethics, the history of AI, the intersection between ethics and computing, the underlying values of AI, privacy concerns around AI applications, different kinds of biases (based on race, gender, age, sexual orientation, faith, geographic location, etc.) associated with many AI applications, the concerns around AI, and associated debates based on contemporary examples. This course will prepare students for systematically analyzing and auditing an AI system for its ethical standards, and designing new systems that are fairer.

Learning Objectives 

• Develop questions situated within the context of ethics in AI

• Apply multiple frameworks to cases

• Provide, receive, and act on constructive critiques of the paper

• Apply systematically analyzing and auditing methodologies 

• Evaluate the AI system for ethical standards

Recommended Books

• Crawford, Kate. “The atlas of AI: Power, politics, and the planetary costs of artificial intelligence.” Yale University Press, 2021.

• Couldry, Nick, and Ulises A. Mejias. “Data colonialism: Rethinking big data’s relation to the contemporary subject.” Television & New Media 20.4 (2019): 336-349.

• Barocas, Solon, Moritz Hardt, and Arvind Narayanan. “Fairness in machine learning.” Nips tutorial 1 (2017): 2. 

• Noble, Safiya Umoja. “Algorithms of oppression.” Algorithms of Oppression. New York University Press, 2018.

• Merry, Sally Engle. “The seductions of quantification.” The Seductions of Quantification. University of Chicago Press, 2016.

Class Evaluation

Additional details on the grading breakdown/rubrics will be available as the semester progresses.

• Class Participation Group: 10%

  • Students will form and discuss in groups.

  • Take summary notes.

  • Due: during every lecture. 

The evaluation is based on the student’s active participation in discussion and question-answering, depth of understanding of the subject matter, skills of connecting different literature, critical analysis ability, and the strength in creative thinking. 

• Bi-weekly Case-Study Group Report: 40% 

  • Due: midnight before the next lecture. 

  • Way to submit: Canvas Assignment Submission Page.

  • Analysis of the case study. The analysis needs to be based on the framework used in the required readings. 

  • Format: 2-2.5 page (without reference) PDF. 11pt. Times New Roman, single-spaced with 1” margin. 

  • Must add a list of individual contributions to the submission.

  • Best 4 reports will be counted. For late submission, 20% will be deducted per day (so, no grade if delayed by 5 days or more).

  • Form 4 to 5 students per group

• Individual Leading Weekly Discussions: 15% 

  • 2 students will form a group and lead class discussion at least once in the semester together. They will together work for presentations

    • summarizing the readings meaningfully (10 + 10 minutes, one paper each), 

    • asking important (5) questions, 

    • showing in class case studies and driving discussion. 

    • The discussion leaders must read all the papers (including the optional readings, if any). 

  • Grading criteria: 

    • Time and style of presentation: 5%

    • Engagement with the content: 5%

    • Quality of questions, case study, and leading discussion: 5%

• Final Project: 35% 

  • Group Project: Form 4 to 5 students per group

  • Proposal: 10%

    • Due Date: 8th week.

    • Format: 1-2 pages PDF

    • Prepresentation: 5 Min presentation + Q&A + peer feedback 

    • Grading criteria:

      • Topic and Motivation (250-500 words): 5%

      • Initial Literature Review (at least 5 papers): 3%

      • Methods and Data (250-500 words): 2%

      • If the initial proposal is not approved, the student will be assigned a project by the instructor. However, their proposal will still be graded according to the above rubrics.

  • Final Presentation: 10%

    • Due Date: Week 16 

    • Format: Maximum 8 minutes presentation, 10 slides

    • Grading criteria:

      • Presenting the full contents in time: 4%

      • The clarity in the presentation: 3%

      • Q&A after the presentation. (2 mins): 3%

  • A full-length research paper: 15%.

    • Due Date: May 9 Noon. 

    • Format: A 15-page PDF (max, without references, minimum 13-pages) in the ACM Master Article Template format (Submission Template).

    • Grading criteria:

      • Literature review: 3%

      • Findings: 5%

      • Discussion and Implication to AI and Critical Data Studies: 5%

      • Paper Formatting, etc.: 2%

Attendance Policy

The instructor will record the attendance through participation in discussion. For planned absence (e.g., religious observance, athletic commitment, interview, among others), students must inform the instructor and may turn in assignments in advance (if they will be absent on the day it is due). For unplanned occasions (e.g., serious illness, family emergency, among others), please contact the instructor immediately.  

Academic Integrity

Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the Code at the following URL: http://studentcode.illinois.edu/. Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy: https://studentcode.illinois.edu/article1/part4/1-401/. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. 

Mental Health

Significant stress, mood changes, excessive worry, substance/alcohol misuse or interferences in eating or sleep can have an impact on academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings which are covered through the Student Health Fee. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do for yourself and for those who care about you.  

• Counseling Center (217) 333-3704 

• McKinley Health Center (217) 333-2700 

• National Suicide Prevention Lifeline (800) 273-8255 

• Rosecrance Crisis Line (217) 359-4141 (available 24/7, 365 days a year) 

If you are in immediate danger, call 911.