Fa22 - INTRO TO ETHICAL AI/ROBOTICS (53145)
Instructors: Peter Stone and Tina L. Peterson
Department of Computer Science
Tuesdays and Thursdays 9:30-10:45am
GDC 1.304
INSTRUCTORS
Peter Stone
office hours: Thursdays 11 am - 12 noon and by appointment
office: GDC 3.508
phone: 471-9796
fax: 471-8885
email: pstone@cs.utexas.edu
Tina L. Peterson
office hours: Tuesdays 11:30 am - 12:30 pm in GDC 4.314 and Wednesdays 1:15 pm - 2:15 pm at https://utexas.zoom.us/j/2526426436
office: GDC 4.314
email: peterson@cs.utexas.edu
TEACHING ASSISTANTS
Caroline Wang
office hours: Mondays 3-4pm at https://utexas.zoom.us/j/4932888912
email: caroline.l.wang@utexas.edu
Yash Kumar
office hours: Tuesdays 2:15 PM - 3:15 PM at https://utexas.zoom.us/j/98230762069
email: yashkumar1803@utexas.edu
COURSE DESCRIPTION
This discussion- and project-based class will introduce students to the ethical issues and challenges that can and do arise when creating AI-based systems. The course will use case studies anchored by readings illustrating the current and future possible uses of automation as well as the broad themes of ethical and responsible AI. We will address how things have gone wrong in the past and open debates regarding how best to design, deploy, and regulate AI systems. We will also incorporate perspectives from diverse human stakeholders whose lives and livelihoods are impacted by AI and automation.
COURSE REQUIREMENTS
Grades will be based on:
- Written responses to the readings and other class participation (10%):
- By 5pm on the afternoon before a class with a new reading assignment due, everyone must submit a one-page (double-spaced) critical response to the assigned readings for that day. Please include your name and eid in the response, which must be uploaded as a PDF to the relevant assignment. In some cases, specific questions may be posted along with the readings. But in general, it is free form. Credit will be based on evidence that you have done the readings carefully. Acceptable responses include (but are not limited to):
- Insightful questions;
- Clarification questions about ambiguities;
- Comments about the relation of the reading to previous readings;
- Solutions to problems or exercises posed in the readings;
- Critiques;
- Thoughts on what you would like to learn about in more detail;
- Possible extensions or related studies;
- Thoughts on the paper's importance; and
- Summaries of the most important things you learned.
These responses will be graded on a 10-point scale with a grade of 10 being a typical full-credit grade for a reasonable response. Responses will be due by 5pm the day before the reading is listed. No late responses will be accepted.
This deadline is designed both to encourage you to do the readings before class and also to allow us to incorporate some of your responses into the class discussions.
Students are expected to be present in class having completed the readings and participate actively in the discussions and activities.
- Attendance and participation (10%):
- In-person attendance is expected. Attendance will be recorded via various means TBD.
- Group-led discussions of week's assigned readings (20%):
- Each week beginning on 9/6, a group of 4 or 5 students will closely read that week's assigned articles/chapters and do a deeper dive on the themes contained within those readings. The group will present what they think is most important within those readings in a format such as a panel discussion, and then lead their peers in a discussion. Plan to spend 20 - 30 minutes on this at the start of class.
- PandemicSim assignments (35%):
-
PandemicSimulator is a novel open-source agent-based simulator that models the interactions among individuals at specific locations within a community. It is intended to provide a more realistic evaluation of potential government policies for pandemic mitigation. With an eye on ethics and fairness, we will 'play with' the simulator and examine policies' impact on people and institutions in the hypothetical simulation environment.
First there will be 3 tutorials as preliminary exercises to enable students to get to know the software. Following the tutorials, students will be asked to propose and implement an extension or modification to PandemicSim that will improve its functionality and/or potential usefulness to city officials. In so doing, the will consider the potential uses and impacts (on different groups) of the modified simulator, as well as any ethical considerations. They will write a report on their modifications and considerations. They will then review and critique each other's reports.
- Stakeholder Interview (25%):
-
The first principle of the ACM Code of Ethics directs computing professionals to “contribute to society and to human well-being, acknowledging that all people are stakeholders in computing.” AI and automation impact human stakeholders every day, increasingly as a tool that they have to use regularly as part of their employment.
Each student will reach out to and interview someone who uses or otherwise interacts with an automated system and/or an AI-driven system as part of their job. A few examples are gig workers, delivery drivers, warehouse workers, and customer service representatives. Students will connect with an interview subject, conduct the interview, record the conversation via smartphone app, Zoom, or some other technology, and then write up an analysis of the interviewee's responses.
PIAZZA
The TAs and instructors will answer questions and post announcements on the course Piazza: piazza.com/utexas/fall2022/cs395t
All students will be responsible for knowing what's posted on Piazza; the information won't be shared elsewhere.
ACCOMMODATIONS FOR DISABILITY
The university is committed to creating an accessible and inclusive learning environment consistent with university policy and federal and state law. Please let us know if you experience any barriers to learning so we can work with you to ensure you have equal opportunity to participate fully in this course. If you are a student with a disability, or think you may have a disability, and need accommodations please contact Services for Students with Disabilities (SSD). Please refer to SSD’s website for contact and more information: http://diversity.utexas.edu/disability/ (Links to an external site.). If you are already registered with SSD, please deliver your Accommodation Letter to one of the instructors as early as possible in the semester so we can discuss your approved accommodations and needs in this course.
EXTENSIONS
If you turn in your assignment late, expect points to be deducted. No exceptions will be made for the written responses to readings-based questions (subject to the "missed work due to religious holy days'' below). For other assignments, TBA.
The greater the advance notice of a need for an extension, the greater the likelihood of leniency.
ACADEMIC DISHONESTY POLICY
You are encouraged to discuss the readings and concepts with classmates. But all written work must be your own. And programming assignments must be your own except when teamwork is authorized. All work ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course. If in doubt, look at the departmental guidelines and/or ask.
MISSED WORK DUE TO RELIGIOUS HOLY DAYS
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
COURSE SCHEDULE AND ASSIGNED READINGS
(Readings will be posted at least one week before they're due)