Course Syllabus

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CS 3960 and PHIL 2150: Ethics in AI

 

Where: MoWe/03:00PM-04:20PM

When: GC 2950

 

Instructors

C. Thi Nguyen c.thi.nguyen@utah.edu

Vivek Srikumar svivek@cs.utah.edu 

 

Teaching Assistant:

Iris Nguyen

 

Office Hours: TBA



This class studies issues in the ethics of AI. How might AI be fair or unbiased? How might it reshape human work and human lives? What responsibility do the designers, deployers, and users of AI have? 

We will study topics including algorithmic bias and fairness, and the impacts of automation on the workplace and our private lives. We will look at what happens when we automate social decision-making, and moral decision-making. We will look at how AI changes the nature of property and ownership, and impacts artistic creativity. We will work through a number of case studies, including chatbot therapists, deepfakes, AI art, and the use of AI in hiring. We will also consider the social and political implications of AI, including environmental impacts and privacy issues. We will

This class is intended both for students with technical expertise in AI, and students with no such background. The class will, along the way, introduce students to the fundamentals in understanding how AI works – how pre-training, training, and deployment works. The class will also introduce students to some foundational ethical and political theories relevant to AI ethics. 

This class is experimental, and fluid, in a number of ways. This is the first time this class is being taught in this form, as a co-taught class, developed and taught in cooperation between CS and Philosophy faculty. The class syllabus is open to revision: we are both willing to make changes in accord with student interest. In addition, the landscape of AI is changing so fast that new and breaking topics may emerge, which may be incorporated into the class.

The learning outcomes of the class are:

  • Basic content mastery of specific issues emerging in AI ethics.
  • Basic content mastery of relevant ethical and political theories.
  • Development of critical thinking skills for reflection, analysis, and 
  • Development of group working skills in communication, collaboration, and collective design. 

In short, half the class is about learning the key ideas, and half the class is about learning the skill of rigorous analysis, argument, communication, and creative problem-solving. 

Readings

All assigned readings are to be done before the class for which they are assigned. All texts are available online, or in the Canvas files section. 

Readings may change during the class, please check Canvas regularly for updates. Readings may change according to both class interest and class votes, and because new issues, readings, and case studies may arise during the class itself. 

Assignments and Grading

The grading structure is:

  • 50% Module exams 
  • 20% Group projects
  • 30% Final project

Module exams: Each module will conclude with a multiple-choice exam assessing mastery of key concepts from both reading and lecture. Most, but not all, key concepts will be discussed in both reading and lecture - so both reading and lecture attendance are required. 

We will drop your lowest module exam grade - so you can, for example, miss one of the exams with no repercussions. 

Group projects: Group projects will be done via a mixture of at-home research and preparation, and in-class synthesis in a workshop environment.

Final projects: The class will end in a multi-part AI Ethics proposal, in which groups will select a problem from the class, and present a proposal for solving it, and critique each others’ proposals. The proposal may take the form of both technical work and policy suggestions. Assessments will include both collaborative written work, and oral examination of team members. 

 

Part II. Course Schedule

Please complete the readings before the class for which they are assigned. 

 

Date

Topics

8/24

Introduction

8/26

Algorithmic Bias

8/31

Algorithmic Bias

9/2

Value-ladenness/Politics

In-class module quiz

9/7

Labor Day. No class

9/9

Value-ladenness/Politics

9/14

Value-ladenness/Politics

  • [TBD]

9/16

Bias revisited

In-class module quiz

9/21

Bias revisited

  • Blodgett, Su Lin, Solon Barocas, Hal Daumé Iii, and Hanna Wallach. 2020. “Language (Technology) Is Power: A Critical Survey of ‘Bias’ in NLP.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, July, 5454–76. https://www.aclweb.org/anthology/2020.acl-main.485.

9/23

Fairness

  • Creel, Kathleen, and Deborah Hellman. 2022. “The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.” Canadian Journal of Philosophy 52 (1): 26–43. https://doi.org/10.1017/can.2022.3.

In-class module quiz

9/28

Fairness

  • Berk, Richard, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2018. “Fairness in Criminal Justice Risk Assessments: The State of the Art.” Sociological Methods & Research, July 2, 0049124118782533. https://doi.org/10/gfgt87.

9/30

Fairness (Pluralism)

  • Joshi, Pratik, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. “The State and Fate of Linguistic Diversity and Inclusion in the NLP World.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, edited by Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.560.

10/5

In-class group project 1

10/7

Work & Meaningfulness

In-class module quiz

10/12

Fall Break. No class

10/14

Fall Break. No class

10/19

Work & Meaningfulness

10/21

Work & Meaningfulness

10/26

Property

In-class module quiz

10/28

Property

  • Cohen, Julie E. 2019. Between Truth and Power (excerpt online) 

11/2

In-class group project 2

11/4

Social Impacts of AI

In-class module quiz

11/9

Social Impacts of AI

11/11

Social Impacts of AI

  • [TBD]

11/16

Corporate AI Ethics

In-class module quiz

11/18

Final project

11/23

Corporate AI Ethics

In-class module quiz

11/25

[Extra topics]

11/30

Final project

12/2

[Extra topics]

12/7

Final project
























**please reserve the final exam period for oral presentation and assessment of final projects**



PART III. OTHER COURSE POLICIES

Office hours: 

We would love to meet with you to chat about the course, your work, and all that. Drop by scheduled office hours, or we are more than happy to make an appointment for in-person or Zoom chats.

 

Professor contact schedule:  

In order to maintain a minimum of work/life balance, we try to keep university-related emailing to a normal work schedule. We have separate personal and university e-mail accounts, and we try to only check my university e-mail account on normal working hours – 9-6, M-F. Don’t expect quick responses to e-mails outside of those hours! In exchange, we have tried to keep the course assignments and deadlines to respect normal working hours for you too – so no Saturday night due dates.

 

Exam rescheduling:

In cases where you will be missing an exam date, please let us know and request alternate accommodations 1 week in advance. (We can grant more last-minute extensions for emergency circumstances, including family stuff and illnesses.) 

 

 

University of Utah Policies

(What follows are university-wide policies for the University of Utah)

Americans With Disabilities Act (ADA)

The University of Utah seeks to provide equal access to its programs, services, and activities for people with disabilities.

All written information in this course can be made available in an alternative format with prior notification to the Center for Disability & Access (CDA). CDA will work with you and the instructor to make arrangements for accommodations. Prior notice is appreciated. To read the full accommodations policy for the University of Utah, please see Section Q of the Instruction & Evaluation regulations.

In compliance with ADA requirements, some students may need to record course content. Any recordings of course content are for personal use only, should not be shared, and should never be made publicly available. In addition, recordings must be destroyed at the conclusion of the course.

If you will need accommodations in this class, or for more information about what support they provide, contact:

Center for Disability & Access

  801-581-5020
  disability.utah.edu
  65 Student Services Building
  201 S 1460 E
  Salt Lake City, UT 84112

Safety at the U

The University of Utah values the safety of all campus community members. You will receive important emergency alerts and safety messages regarding campus safety via text message. For more safety information and to view available training resources, including helpful videos, visit safeu.utah.edu.

To report suspicious activity or to request a courtesy escort, contact:

Campus Police & Department of Public Safety

  801-585-COPS (801-585-2677)
  dps.utah.edu
  1735 E. S. Campus Dr.
  Salt Lake City, UT 84112

Addressing Sexual Misconduct

Title IX makes it clear that violence and harassment based on sex and gender (which includes sexual orientation and gender identity/expression) is a civil rights offense subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, color, religion, age, status as a person with a disability, veteran’s status, or genetic information.

If you or someone you know has been harassed or assaulted, you are encouraged to report it to university officials: 

Title IX Coordinator & Office of Equal Opportunity and Affirmative Action

  801-581-8365
  oeo.utah.edu
  135 Park Building
  201 Presidents' Cir.
  Salt Lake City, UT 84112

Office of the Dean of Students

  801-581-7066
  deanofstudents.utah.edu
  270 Union Building
  200 S. Central Campus Dr.
  Salt Lake City, UT 84112

To file a police report, contact:

Campus Police & Department of Public Safety

  801-585-COPS (801-585-2677)
  dps.utah.edu
  1735 E. S. Campus Dr.
  Salt Lake City, UT 84112

If you do not feel comfortable reporting to authorities, the U's Victim-Survivor Advocates provide free, confidential, and trauma-informed support services to students, faculty, and staff who have experienced interpersonal violence.

To privately explore options and resources available to you with an advocate, contact:

Center for Campus Wellness

  801-581-7776
  wellness.utah.edu
  350 Student Services Building
  201 S. 1460 E.
  Salt Lake City, UT 84112

Academic Misconduct

It is expected that students comply with University of Utah policies regarding academic honesty, including but not limited to refraining from cheating, plagiarizing, misrepresenting one’s work, and/or inappropriately collaborating. This includes the use of generative artificial intelligence (AI) tools without citation, documentation, or authorization. Students are expected to adhere to the prescribed professional and ethical standards of the profession/discipline for which they are preparing. Any student who engages in academic dishonesty or who violates the professional and ethical standards for their profession/discipline may be subject to academic sanctions as per the University of Utah’s Student Code: Policy 6-410: Student Academic Performance, Academic Conduct, and Professional and Ethical Conduct.

Plagiarism and cheating are serious offenses and may be punished by failure on an individual assignment, and/or failure in the course. Academic misconduct, according to the University of Utah Student Code:

“...Includes, but is not limited to, cheating, misrepresenting one’s work, inappropriately collaborating, plagiarism, and fabrication or falsification of information…It also includes facilitating academic misconduct by intentionally helping or attempting to help another to commit an act of academic misconduct.”

For details on plagiarism and other important course conduct issues, see the U's Code of Student Rights and Responsibilities.



Course Summary:

Course Summary
Date Details Due