Ethics for AI and Robotics (EECS 598/498, ROB 599)

Course Description

Artificial Intelligence, Machine Learning, and Robotics have seen dramatic progress in the last several decades. There is increasing excitement and apprehension about the impact of these technologies, deployed in our world and our human society.

Ethics is the discipline within philosophy that considers which actions we humans see as right or wrong, or as good or bad. As we design intelligent artifacts that make their own decisions about how to act, and as they act within the human world, we ask how we can ensure that they will act ethically.

Two important questions arise.

First, like any other powerful technology (e.g. nuclear power, genetic engineering), there are important ethical questions about how AI and robotics technology can and should be deployed, and what its impact will be on society. This topic includes regulations, and the processes by which regulations are proposed, adopted, and enforced.

Second, unlike other technologies, AI (and thus intelligent robotics) involves creating agents that make their own decisions about how to act in the world. Ethics is a kind of foundational knowledge that humans use to decide how to act. We need to understand the structure of that knowledge, so the AIs we create will have the knowledge they need to act appropriately.

Do we mean that humans must be ethical as we design and deploy intelligent systems? Do we mean that the systems we design and deploy must be capable of deciding what is ethical for them to do? Most likely, the answers to both questions will turn out to be “Yes!” The follow-on question is “How do we do that?”

The semester will be organized around several major topic areas:

In the course of discussing research on these problem areas, we will draw on concepts from philosophical ethics, and from engineering design, law, economics, evolution, history, human development, etc.

An important question for researchers in artificial intelligence and robotics is how the knowledge relevant to making ethical decisions can be represented computationally in a knowledge base, and how it can be acquired.

Class meetings

Lectures: two each week, 80 minutes each (MW 4:30 - 5:50 pm).
Discussion: one each week, 50 minutes (F 3:30 - 4:20 pm).
(Attendance will be taken, and counted toward the Participation grade.)

We anticipate having a number of guest lecturers.

The lectures will include opportunities for class discussion. The Discussion meeting will go into further depth on issues that come up in lectures, but may also explore important issues in current news. Some discussions will involve the entire class, but we may also break into small groups.

What if you must miss attending class?

Attendance does count toward the Participation grade (and so does interaction on Piazza). I recognize that a modest number of absences are inevitable and will not be penalized. Large numbers of absences are a problem, though.


Course requirements

Each student will attend the classes, participate in the discussions, and write two papers. Attendance and participation will have significant weight (20%) in the course grade.

Pick a topic for your two term papers by the end of January (1-30-2023). Don't restrict yourself to what we've already covered in the course. Look ahead in the schedule, or come up with something else. Submit for comments and advice, but not counted toward the course grade.

In the first paper, due at mid-term (2-22-2023), you will formulate a question and review the available literature related to that question. The goal of your paper is to identify, clarify, and summarize the major positions on that question. Counts for 40% of the course grade.

In the second paper, due at the end of the term (4-17-2023), you will pick a question, take a position on how it should be answered, and justify your position, responding clearly to anticipated arguments from critics of that position. Counts for 40% of the course grade. Focusing either or both papers on the topic submitted in January is not required, but it will obviously make both papers stronger.

The following paper is a good example of a literature review, and it is also highly relevant to the content of the course. Read it before the start of the course.

When you write your own literature review, it need not match this in length or style, but this paper is an aspirational target. For your paper, imagine that you are providing detailed help on a particular focused topic, to a friend who wants to get started doing research on that specific topic.

Course readings

There will be extensive assigned readings, and you will need to find and read additional papers and possibly books as part of your literature review and final term paper.

Be sure that you know how to use Google Scholar and the UM Library's Online Journal collection for tracking down references.

Course format

This course is offered to multiple audiences through several course numbers.

On Collaboration

Students learn more when they collaborate actively with each other. But you also learn more when you take responsibility for doing your own work. Be prepared to teach each other, and to learn from each other, but not to do each others' work.

Discuss all the topics in the course, but especially the topics of your papers, with each other. Recommend papers to each other. Read and comment on each others' drafts as alpha and beta readers. But formulate and defend your own opinions, and write your own papers.

It is not a course requirement that you publish your paper in a conference or journal, but that's certainly a great outcome! After the course is over, it is entirely reasonable to combine and revise closely related papers into a joint submission for publication.

Platform for Questions and Online Discussion

We have tried several platforms for online discussion outside of class. This year, we will use Piazza again. If you have an opinion on this, feel free to let me know.

Suggested readings . . .