ECSE 557

Introduction to Ethics of Autonomous Intelligent Systems

About the Course

Offered at the Dept. of Electrical and Computer Engineering, McGill University

Upcoming Offering: Winter 2023

Credits: 3 credits

Activity schedule (per week, for 13 weeks): 2x1.5 hour lectures, 1 hour tutorial, 5 hours of self-study

Prerequisites: (ECSE 202 or COMP 250 (Introduction to Computer Science)) and (ECSE 205 (Probability and Statistics for Engineers) or MATH 323 (Probability)) or instructor approval

Proposed Co-requisite: COMP 451 (Fundamentals of Machine Learning) or COMP 551 (Applied Machine Learning) or ECSE 551 (Machine Learning for Engineers).

NOTE: Co-requisite means you need to have taken these courses already or will be taking the course(s) alongside ECSE557. If you are a non-McGill student looking to take the course through inter-university credit transfers etc., or are graduate students with similar course credits from other institutions, please send me an email about it.

Recommended but not required: COMP 424 (Artificial Intelligence), ECSE 526 (Artificial Intelligence)

Past offering: Winter 2022 (Course outline),

FAQs

Who is this course for?

This course is intended for engineering undergraduate students who are interested in learning about the social and ethical implications of intelligent systems. It is also intended to provide a richer selection of courses for students pursuing a minor in AI or interested in pursuing a career in AI.

Is there a textbook for the course?

There are no assigned textbooks for the course. However, students wanting to have more reference material to follow alongside the lectures/tutorials, you'll find the following books helpful (all of these are open access or avail. as e-books via McGill library):

Chapters from Spiekermann, S. (2016), Ethical IT Innovation: A Value-Based System Design Approach
Chapters from Barocas S., Moritz H., Narayanan A. (2019) Fairness and Machine Learning
Chapters from Nielsen A. (2020), Practical Fairness: Achieving Fair and Secure Data Models

schedule

The following is a rough list of topics I attempt to cover. But every semester is a little different. It will likely change as we go, especially as students bring their own interests to the table. For now, the most accurate plans for the topics to be covered can be found on the course outline listed above.

Part I – Fundamentals

Week 1 – Fundamentals

Lecture 1: First class, A brief review of ethics

Intro to the course. How is the course different from professional ethics, engineering codes of conduct, research ethics, and other ethics discussions? Why is it important? A quick review on the three (western) schools of thought in ethics; technology ethics and where does moral psychology fit in?

Lecture 2: A brief introduction to AI

What are autonomous intelligent systems, and what’s the difference between AI, ML, robotics?

Tutorial 1: Worked examples on ethics theories

Week 2 – AI ethics principles & values

Lecture 1: Values I & Human Rights

What are values? What are normative values? What are principles/standards? The Montreal AI Declaration. What are the human rights and what’s in the UDHR?

Lecture 2: Values II

Conceptualizing key values: responsibility and accountability; fairness, equality, bias & diversity; truth, trustworthiness, & trust; transparency, explainability, interpretability; automation & autonomy; privacy and human rights; Identifying and prioritizing values.

Tutorial 2: Worked examples on value identification and prioritization

Week 3 –Data

Lecture 1: Ethically sensitive data & informed consent

Personally identifiable data; what makes data sensitive? What data can/can’t you collect? What are protected data? Components of informed consent;

Components of data quality; secondary use of data; data mining; case studies

Lecture 2: Anonymity & privacy

Anonymity and pseudonymity; k-anonymity; l-diversity principle; privacy guarantees

Tutorial 3: Worked examples on k-anonymity, and l-diversity principles

Part II – AI Ethics

Week 4 – Information and knowledge

Lecture 1: Algorithms for sharing and recommending

Knowledge & knowledge sharing (social media); truth and objectivity; recommender engines and filter bubbles; online advertisement; case study on Microsoft Tay

Lecture 2: Midterm 1 – Part I: Fundamentals

Midterm exam during class time

Tutorial 4: Midterm review

Week 5 – Algorithmic Fairness

Lecture 1: Bias and Fairness I

Machine bias; fairness as a concept, conditions for fairness perception; fairness definitions and metrics; transparency and recourse; case study

Lecture 2: Bias and Fairness II

Case study

Tutorial 5: Fairness evaluation worked example

Week 6 – Privacy

Lecture 1: Privacy as a concept

What is privacy? Who has privacy? What are privacy harms and what do we do with them? Privacy laws in Quebec and Canada, and GDPR; surveillance technologies; facial recognition; anonymity in public spaces; encryption and politics;

Lecture 2: Considering privacy in design

Using privacy assessment tools; privacy by design; concepts behind privacy-preserving techniques (differential privacy, federated learning)

Tutorial 6: Worked case studies on privacy

Week 7 – AIS that interact with people

Lecture 1: Automation bias and shared control

Automation bias, human factors, shared control, accountability gap. Case studies

Lecture 2: User preference and manipulation

Types and components of dark patterns, positive vs. negative liberty; behaviourism in technology; nudging;

Tutorial 7: Dark patterns worked example

Week 8 – Ethics in the design of AIS

Lecture 1, 2: Ethics analysis processes and ethics in design practices

Stakeholder identification; datasheets for datasets; design methods; AI ethics principles; toolkits; ethically aligned design. Case study.

Tutorial 8: Ethics analysis worked example I

Part III – Robot Ethics

Week 9 – Robots vs. algorithms

Lecture 1: Robots vs. algorithms

Introduction to service and consumer robotics; how the physical robot complicates ethics issues

Lecture 2: Human-robot interaction

Anthropomorphism; Uncanny Valley theory; racializing and gendering of robots; trust and manipulation in HRI; owners vs. clients; individual vs. public; case study

Tutorial 9: Ethics analysis worked example II

Week 10 – Robot ethics issues

Lecture 1, 2: Issues in killer robots, autonomous cars, care robots

What do the real lethal autonomous weapons systems (LAWS) look like? International humanitarian law and the LAWS debate; rules of engagement, laws of war; Levels of automation; ethics dilemmas; design assumptions; Robots for eldercare and childcare;

Tutorial 10: Project support session

Week 11 – Artificial moral agents

Lecture 1: Artificial moral systems, top-down AMA

What is an AMA? Types of moral systems; science fiction vs. reality; top-down, bottom-up, and hybrid systems; Design of a top-down AMA; why the Asimov’s Three Laws of Robotics don’t work

Lecture 2: Bottom-up & hybrid AMA

Utilitarian calculus and machine learning; learning from user preferences and behaviours; virtue ethics in AMA

Tutorial 11: Project support session

Week 12 – AIS and society

Lecture 1: Automation, economics, and automated inequality

Discussions on what economists say about the role automation plays in society; Do AIS lead to more inequality in our society? Case study-driven discussion

Lecture 2: Power and technology activism

Case study-driven discussion

Tutorial 12: Project support session

Week 13 – Project presentations

Lecture 1: Project presentations I

Lecture 2: Project presentations II & course debrief

Tutorial: none