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This discussion-based course will provide an introduction to the ethical issues related to artificial intelligence and machine learning. The first half of the semester will focus on concerns existing in the present day, such as bias and transparency. During the semester's second half, we will cover topics that will be increasingly important going forward, from consciousness to the future of labor.

• Course: CMSC389V
• Prerequisites: CMSC216 and CMSC250
• Credits: 1
• Seats: 30
• Lecture Time: Friday, 1:00-1:50 pm
• Location: MTH0101
• Course Materials: All required course materials will be provided.
• Course Facilitator: Anthony Ostuni
• Faculty Advisor: Dr. John Dickerson

This course will primarily be discussion-based, with the topic of each week's discussion determined by a set of short readings and/or videos. These materials must be completed before class and used to write a response to a short-answer question. Note that the materials do not necessarily reflect the viewpoints of the course facilitator nor faculty advisor; they are simply design to provoke thought on the subject area. Prior to the class discussion, there may be a brief lecture on a specific idea or concept that could be of value to the conversation. The primary assignments for the class will be two papers.

Grades will be maintained on ELMS.

You are responsible for all material discussed during lecture, as well as readings and other material posted on ELMS or Piazza outside of class.

The majority of time in the classroom will be spent in group discussion. This will allow for an efficient exchange of diverse ideas and perspectives, as well as forcing you to become more comfortable organizing and articulating technical and philosophical concepts. We will decide discussion rules together as a class during the first week.

There will be two essays that will compose the majority of your work outside the classroom. The first will be assigned after Week 6, and it will be on one of the topics discussed up to that point in class. The second paper will be assigned after Week 12, and it should represent the culmination of ideas developed throughout the previous six weeks. The official assignment details for both papers will be released on ELMS.

All regrade requests must be made within one week of the assignment grade being released.

In lieu of a final, you will be expected to develop a list of ML / AI Guidelines for a tech company to follow and justify your rules. The official assignment details will be released on ELMS.

All assignments may be turned in up to 24 hours late with a 25% penalty. After 24 hours, no late assignments will be accepted.

The schedule is subject to change; students will be notified in such an occurrence.

The primary means of communication outside of class will be Piazza and ELMS. Office hours will be scheduled by appointment. The email addresses below should only be used for important and time-sensitive issues.

• Faculty Adviser: Dr. John Dickerson: john [at] umd.edu
• Course Facilitator: Anthony Ostuni: aostuni [at] umd.edu

See the section titled "Attendance, Absences, or Missed Assignments" available at Course Related Policies.

See the section titled "Accessibility" available at Course Related Policies.

Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct.

It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.

If you have any suggestions for improving this class, don't hesitate to tell the instructor or facilitators during the semester. At the end of the semester, please don't forget to provide your feedback using the campus-wide CourseEvalUM system. Your comments will help make this class better in future iterations.