Schedule

We do not expect students to master the background readings or even necessarily be familiar with the contents of the background reading. These are included just in case any students want additional resources beyond the lectures, particularly as we discuss potential projects. We will ensure book chapters are made available on CANVAS. All the papers with “*” are available from teamcore.seas.harvard.edu.

Part I: Introduction to AI and AI ethics

Jan 27 (L1): Course overview, perspectives on AI and Ethics, fairness in the data-to-deployment pipeline, collaborative problem solving

Jan 29 (L2): Introduction to game theory, decision theory, Machine Learning

Feb 3 (L3): Introduction to AI ethics (case study: MIT Media Lab’s Moral Machine project)

Abby Jaques, “Why the Moral Machine is a Monster”
(Background) Play the Moral Machine Experiment*
(Background) Karen Hao, “Should a self-driving car kill the baby or the grandma? Depends on where you’re from”

Part II: AI and Public Health

Feb 5 (L4): AI approaches to network-based prevention I (case study: Have You Heard?)

“PSINET: Assisting HIV Prevention Amongst Homeless Youth by Planning Ahead” A. Yadav et al, AI Magazine, Vol 37 (2), 2016**
“Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application” A. Yadav et al, AAMAS 2017**
“Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)” Rahmattalabi et al https://arxiv.org/abs/1902.00171
(Background) “Causes and consequences of youth homelessness”, E. Rice & H. Winetrobe, in “Artificial Intelligence and Social Work”, Editors Rice & Tambe

Feb 10 (L5): Evaluating outcomes in public health

Shafer-Landau, The Fundamentals of Ethics, “Consequentialism”
John Harris, “QALYfying the Value of Life”

Feb 12 (L6): Public health, inequality, and AI (guest speaker: Kasisomayajula Viswanath)

Feb 17: No class (President’s Day)

Feb 19 (L7): AI approaches to network-based prevention III; respecting rights in public health (case study: TND Network)

Will Kymlicka, “Moral Philosophy and Public Policy: The Case of NRTs”
Robert Nozick, Anarchy, State, and Utopia, “Goals and Constraints”

Feb 24 (L8): Discussion of Assignments

Feb 26 (L9): Introduction to fair machine learning (case study: Instant Checkmate)

Solon Barocas, Moritz Hardt, and Arvind Narayanan, Fairness in Machine Learning, Introduction
Latanya Sweeney, “Discrimination in Online Ad Delivery”
(Background) Andrew Altman, “Discrimination” (Stanford Encyclopedia of Philosophy)

Mar 2 (L10): AI approaches to tuberculosis prevention and suicide prevention (case study: Teamcore group TB project, suicide prevention project)

“Exploring Algorithmic Fairness in Robust Graph Covering Problems” Rahmtallabi et al, NeurIPS 2019**
“Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data” Killian et al, KDD 2019**
“Group-Fairness in Influence Maximization” Tsang et al, IJCAI 2019

Mar 4 (L11): Fair machine learning in public health (guest speaker: Shahin Jabbari)

Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations”
Andrew Selbst, dana boyd, Sorelle Friedler, Suresh Venkatasubramanian, and Janet Vertesi, “Fairness and Abstraction in Sociotechnical Systems”

Mar 9 (L12): Ethics in product development (guest speaker: Ece Kamar)

Mar 11 (L13): Project proposal presentations

March 13: FINAL project proposals due

Mar 16 & 18: No class (Spring Recess)

Mar 23 (L14): Human subjects research ethics (guest speaker: Mary Gray; case study: Facebook’s emotion contagion experiment)

Part III: AI and Public Safety

Mar 25 (L15): Predictive policing (guest speaker: TBA)

Mar 30 (L16): Fairness in recidivism prediction I (case study: COMPAS)

Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, “Machine Bias”
Alexandra Chouldechova, “Fair prediction with disparate impact: A study of bias in recidivism prediction instruments”
(Background) Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin, “How We Analyzed the COMPAS Recidivism Algorithm”
(Background) Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores”

April 1 (L17): Fairness in recidivism prediction II (case study: COMPAS)

Robert Long, “Against False Positive Rate Equality”
(Background) Aravind Narayanan, “21 Fairness Definitions and their Politics” (video)

April 6 (L18): TBA

April 8 (L19): guest speaker: Desmond Patton

Part IV: AI and Wildlife Conservation

April 13 (L20): AI approaches to wildlife conservation II(case study: PAWS)

“Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations.” Xu et al ICDE 2020** https://arxiv.org/abs/1903.06669
“Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version)” Gholami et al, AAMAS 2018**
“SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time” Bondi et al, IAAI 2018**
“Strategic Coordination of Human Patrollers and Mobile Sensors with Signaling for Security Games” Xu et al, AAAI 2018**

April 15 (L21): Moral responsibility for unintended uses of technology (case studies: PAWS, OpenAI GPT-2) [David]

Heather Douglas, “The Moral Responsibilities of Scientists (Tensions “between Autonomy and Responsibility)”
Alec Radford et al., “Better Language Models and Their Implications”
(Background) Irene Solaiman et al., “Release Strategies and the Social Impacts of Language Models”

CS 108: Intelligent Systems: Design and Ethical Challenges

Spring 2020

Schedule