Readings

Reading List and Course Outline

Below is a schedule of class topics and readings. 

They are subject to change by the instructors, and all changes will be communicated.

You should be able to find links and website information to most readings. Be sure to access the links on Notre Dame network/VPN to access these journals. Some required readings are book chapters that you can obtain through a bookstore. Often, preprint versions of books are available online as well.  

PART1: INTRODUCTION AND FRAMEWORKS

Class 1. Wednesday, August 24: Opening session (no pre-reading)

Class 2. Monday, August 29: What is AI and Why it matters?

• J. McCarthy et al., A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (1955), http://people.csail.mit.edu/brooks/idocs/DartmouthProposal.pdf

 (possibly the first use of the term “artificial intelligence”)

• Gideon Lewis-Kraus, The Great AI Awakening, N.Y. TIMES MAG. (Dec. 14, 2016), https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html
• Brynjolfsson, Erik and Andrew McAfee (2017). “The Business of Artificial Intelligence: What it Can—and Cannot—Do for Your Organization.” Harvard Business Review, July. (https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-Artificial-Intelligence.pdf)

Class 3. Wednesday, August 31: Why should we care and how? Codes of Ethics, Disciplinary Perspectives, and Case Studies

• Existing Codes of Ethics: ACM Code of Ethics and Professional Conduct, Artificial Intelligence at Google – Our Principles, Ethical OS Risk Mitigation Checklist
• “Feynman’s Error: On Ethical Thinking and Drifting” by Dan Munro (Dan’s blog, November 2018)
• “Optimize What?" by Jimmy Wu
  • “Of Course Congress Is Clueless About Tech—It Killed Its Tutor” (WIRED, 2016)

Supplementary:

• “Solving for Pattern” by Wendell Berry (Chapter 9 in The Gift of Good Land: Further Essays Cultural & Agricultural, North Point Press, 1981)
• “Data Science as Political Action: Grounding Data Science in a Politics of Justice” by Ben Green (2019)

Class 4. Monday, September 5: Technical Primer 1: Machine Learning

•  “Introduction to Probability and Machine Learning” by Mehran Sahami (2021), on Canvas
• Machine Learning a Primer by Lizzie Turner (Medium) https://medium.com/@lizziedotdev/lets-talk-about-machine-learning-ddca914e9dd1

Supplementary

• AI/ML Primer by ACT/IAC https://www.actiac.org/system/files/Artificial%20Intelligence%20Machine%20Learning%20Primer.pdf

Class 5. Wednesday, September 7: Technical Primer 2: State of AI

• One Hundred Year Study on Artificial Intelligence (Stanford, 2021): Read SQ1-SQ6, SQ9-SQ10. Rest is optional but a good read.
• Optional: 2016 Report Appendix I (pp. 50-52), for those who would like a short history of AI

Class 6. Monday, September 12: Technical Primer 3: State of the Art – Foundation Models

• On the Opportunities and Risks of Foundation Models (Percy Liang et al., 2022): Read 1. Introduction. https://arxiv.org/pdf/2108.07258.pdf
• OpenAI’s new language language generator GPT-3 is shockingly good (MIT Technology Review) https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/

Supplementary

• https://www.technologyreview.com/2022/01/27/1044398/new-gpt3-openai-chatbot-language-model-ai-toxic-misinformation/

PART 2: APPLICATION OF AI AND ETHICAL ISSUES

Class 7. Wednesday, September 14: Fairness

• “Machine Bias” by Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner (ProPublica, 2016)
• “Algorithms, Correcting Biases” by Cass Sunstein (Social Research, 2019)

• Can you make AI fairer than a judge? Play our courtroom algorithm game” by Karen Hao and Jonathan Stray (MIT Technology Review, 2019)

Supplementary:

• “When an Algorithm Helps Send You to Prison" by Ellora Israni (New York Times, 2017)

Class 8. Monday, September 19: Fairness

• John Rawls, A Theory of Justice, pp. 10-24, Section 3 “The Main Idea of the Theory of Justice,” Section 4 “The Original Position and Justification,” and Section 5 “Classical Utilitarianism,” (Harvard University Press, 1971; revised 1999). On Canvas.
• Case Study: Algorithmic Decision-Making and Accountability. On Canvas.

Supplementary:

• Video Explainer on Rawls’ Original Position (Wireless Philosophy, 2014)
• “Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights,” pp. 1-18, 22-24 (White House, May 2016)
• Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Introduction and Chapter 1 (Crown Publishing Group, 2016)

Class 9. Wednesday, September 21: Discrimination

• “Discrimination in the Age of Algorithms” by Kleinberg et al, Sections I and II. Rest is optional (Journal of Legal Analysis, 2018)
• Buolamwini, Joy and Timnit Gebru (2018). “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of Machine Learning Research, Vol. 81, pp. 1–15. (http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf)

Supplementary:

• This is how AI bias really happens and why it’s so hard to fix (MIT Technology Review, 2019) https://www.technologyreview.com/2019/02/04/137602/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/
• “Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms” by Nicol Lee, Paul Resnick, and Genie Barton (Brookings Institution, 2019)

Class 10. Monday, September 26: AI and the Future of Work 

• “The future of work in America.” McKinsey Global Institute, pp. 1–21 (2019),
• Ajay Agrawal, Joshua Gans, and Avi Goldfarb (2019). “Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction.” Journal of Economic Perspectives. (https://pubs.aeaweb.org/doi/pdf/10.1257/jep.33.2.31)

Supplementary 

• The economics of artificial intelligence: Implications for the future of work. ILO Future of Work Research Paper Series. 2018. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_647306.pdf 

Class 11. Wednesday, September 28: AI and Global Labor

• “AI and the Global South: Designing for Other Worlds.” Chinmayi 2019. 
• “The invisible workers of the AI era.” Towards Data Science (Dec. 12, 2018)
• “How Cheap Labor Drives China’s AI Ambitions.” NYTimes (Nov. 25, 2018).
• “Empathetic Robots are Killing Off the World’s Call Center Industry.” Bloomberg Businessweek (March 16, 2021)

Supplementary

• The human labor behind artificial intelligence. Marketplace Morning Report (May 4, 2021)

Class 12. Monday, October 3: Data Collection and Privacy - Biometrics

• Case Study: Facial Recognition. On Canvas
• Do we actually agree to these terms and conditions 

https://blogs.ischool.berkeley.edu/w231/2021/07/09/do-we-actually-agree-to-these-terms-and-conditions/

• “Civil Society Letter to Amazon on Facial Recognition” (Human Rights Watch, 2019)

Supplementary

• “The End of Trust” from McSweeney’s and Electronic Frontier Foundation
• “The Perpetual Line-Up: Unregulated Police Face Recognition In America” from Center on Privacy and Technology at Georgetown Law
• Podesta report “Big Data: Seizing Opportunities, Preserving Values" (especially pp. 58-68)
• "Report on the Telephone Records Program Conducted under Section 215" (Privacy And Civil Liberties Oversight Board, 2014)
• “Facial recognition technology: The need for public regulation and corporate responsibility” by Brad Smith (Microsoft, 2018)

Class 13. Wednesday, October 5: Data Collection and Privacy

• "Privacy and human behavior in the age of information” by Alessandro Acquisti, Laura Brandimarte, and George Loewenstein (Science, 2015)
• "Privacy and Information Sharing” by Lee Rainie and Maeve Duggan, pp. 1-8 (skim the rest), (Pew Research Center, 2016)
• "Americans feel the tensions between privacy and security concerns” by Shiva Maniam (Pew Research Center, 2016)
• Kostka, G. (2019) ‘China’s social credit systems and public opinion: Explaining high levels of approval’, New Media and Society, 21(7), pp. 1565–1593. doi: 10.1177/1461444819826402. 

Supplementary:

• Collis et al. (2021). Quantifying the user value of social media data. https://www.law.upenn.edu/live/files/11670-quantifying-the-user-value-of-social-media-data
• Park et al. “Information Technology–Based Tracing Strategy in Response to COVID-19 in South Korea—Privacy Controversies” JAMA. 2020;323(21):2129-2130. doi:10.1001/jama.2020.6602 https://jamanetwork.com/journals/jama/fullarticle/2765252

• “Why ‘Anonymous’ Data Sometimes Isn’t” by Bruce Schneier (WIRED, December 2007)

• “Nudging Privacy: The Behavioral Economics of Personal Information” by Alessandro Acquisti (IEEE Security & Privacy, 2009)
• “Privacy and Data Protection in an International Perspective” by Lee A. Bygrave, sections 3-5 (Scandinavian Studies in Law, 2010)

• “Comparing Privacy Laws: GDPR v. CCPA” by DataGuidance and Future of Privacy Forum (2018)
• GDPR, Art. 5 “Principles relating to processing of personal data”

Class 14. Monday, October 10: March 2: Safety - Autonomous Driving Systems 

• Case Study: Autonomous Vehicles, on Canvas.
• Congressional Research Service, Issues in Autonomous Vehicle Deployment (2020), https://fas.org/sgp/crs/misc/R45985.pdf 
• Ben Dickson, Why Deep Learning Won’t Give Us Level 5 Autonomy in Self-Driving Cars, Techtalks blog (July 29, 2020), https://bdtechtalks.com/2020/07/29/self-driving-tesla-car- deep-learning/ 

Supplementary:

• "Never Mind the Trolley: The Ethics of Autonomous Vehicles in Mundane Situations" by Johannes Himmelreich (Ethical Theory and Moral Practice, 2018)

• “The Pursuit of the Ideal” by Isaiah Berlin (ch. 1 from The Crooked Timber of Humanity)

• "Isaiah Berlin: Against Dogma” by Henry Hardy (Times Literary Supplement, 2020)

Class 15. Wednesday, October 12: Case-study presentations

• Case-study presentations in round table format

MIDTERM BREAK

PART 3. AI ETHICS AND GOVERNANCE IN THE REAL WORLD

Class 16. Monday, October 24: Careers in AI Governance and Policy

• Guest speaker, Benjamin Larsen, Project Lead, World Economic Forum 

Class 17. Wednesday, October 26: Rome Call for AI Ethics: A Global University Summit

• Research keynote and panel session during class time
• Encouraged to attend other sessions

PART 4. AI, GLOBAL AFFAIRS, AND POLITICS

Class 18. Monday, October 31: AI and (Global) Inequality

• “The Limits of Global Inclusion in AI Development.” Chan et al. 2021

• “Harnessing Artificial Intelligence for Development on the Post-COVID-19 Era : A Review of National AI Strategies and Policies.” World Bank 2021.

• “Modeling the Impact of AI on the World Economy”, McKinsey Global Institute 2018.
• “How Artificial Intelligence Could Widen the Gap Between Rich and Poor Nations.” IMF Blog 
• “The AI Link Between Global Inequality and Your Bank Balance” Towards Data Science (2019)
• “Will AI Revolution Cause a Great Divergence?” IMF Working Paper (2020). Sections 1 and 2 only.

Supplementary:

• Lee, Kai-Fu (2018). “The Four Waves of AI.” White paper adapted from AI Superpowers: China, Silicon Valley, and the New World Order. Boston: Houghton Mifflin. (http://storage.googleapis.com/aisp-assets/pdf/the-four-waves-of-ai.pdf?mtime=20180926123520)
• Korineck and Stiglitz. 2018. Artificial Intelligence and Its Implications for Income Distribution and Unemployment. NBER Working Paper 24174. Sections 1, 2, and 3.

Class 19. Wednesday, November 2: AI and International Development

• "Improving Refugee Integration through Data-Driven Algorithmic Assignment" by Kirk Bansak, Jeremy Ferwerda, Jens Hainmueller, Andrea Dillon, Dominik Hangartner, Duncan Lawrence, Jeremy Weinstein (Science, 2018)
• Using big data and artificial intelligence to accelerate global development. Brookings Report (Nov. 15, 2018)

• “Artificial Intelligence in International Development: Avoiding Ethical Pitfalls.” Anderson.

• Examine some of the projects at Data Science for Development. Center for Effective Global Action. 
  • https://medium.com/center-for-effective-global-action/gender-differentiated-credit-scores-bridging-the-gender-gap-in-access-to-credit-87e040318cdb
  • https://www.jblumenstock.com/files/papers/mappingpoverty_2017_JRSI.pdf
  • https://www.science.org/doi/10.1126/science.aaf7894
  • https://dil.berkeley.edu/

Class 20. Monday, November 7: Information and Polarization

• “Exposure to ideologically diverse news and opinion on Facebook” by Eytan Bakshy, Solomon Messing, Lada A. Adamic (Science, 2015)
• “Personalized News Recommendation Based on Click Behavior” by Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen (Google, 2009). https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35599.pdf
• “Trends in the Diffusion of Misinformation on Social Media” by Hunt Allcott, Matthew Gentzkow, Chuan Zu (2018)
• “Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature” by Josh Tucker (Hewlett Foundation, 2018). Skim through executive summaries.

Class 21. Wednesday, November 9: AI and Authoritarianism

• Ünver, H. Akın (2018). “Artificial Intelligence, Authoritarianism and the Future of Political Systems.” Cyber Governance and Digital Democracy 2018/9 Paper, Centre for Economics and Foreign Policy Studies, July. (https://edam.org.tr/wp-content/uploads/2018/07/AKIN-Artificial-Intelligence_Bosch-3.pdf)
• Yang, David (2019), “The impact of media censorship,” American Economic Review. https://www.aeaweb.org/articles?id=10.1257/aer.20171765. Read Introduction, Sections 1 and 2.
• NPR. https://www.npr.org/2021/01/05/953515627/facial-recognition-and-beyond-journalist-ventures-inside-chinas-surveillance-sta

Supplementary

• Lee et al. 2021. US-China Tech Competition and the Willingness to Share Personal Data in China. On Canvas.

PART 5. AI GOVERNANCE & POLICY

Class 22. Monday, November 14: Power of Private Platforms | Tensions & Trade-offs

• Case Study: Platforms, on Canvas.
• “The Social Responsibility of Business is to Increase its Profits” by Milton Friedman (The New York Times Magazine, 1970)

Class 23. Wednesday, November 16: Regulating AI

Richard Posner, Regulation (Agencies) Versus Litigation (Courts): An Analytical Framework, in Regulation versus Litigation: Perspectives from Economics and Law 11–22 (Daniel P. Kessler, ed., 2011)

• “Litigating Algorithms 2019 U.S. Report: New Challenges to Government Use of Algorithmic Decision Systems” by Rashida Richardson, Jason M. Schultz, and Vincent M. Southerland (AI Now Institute, 2019)
• “Society-in-the-Loop” by Iyad Rahwan (ArXiv, 2017)

Supplementary:

• “The Scored Society: Due Process for Automated Predictions” by Danielle Citron and Frank Pasquale, pp. 18-33 (Washington Law Review, 2014)
• Does Information About AI Regulation Change Manager Evaluation of Ethical Concerns and Intent to Adopt AI? (Mariano-Florentino Cuellar, Ben Larsen, Yong Suk Lee, and Michael Webb). Journal of Law, Economics, and Organization. 2022.

Class 24. Monday, November 21: Government by Algorithms

• David Freeman Engstrom et al., Government By Algorithm: Artificial Intelligence In Federal Administrative Agencies 6–13, 22–29, 37–45, 75–78, 82–85 (2020), https://www-cdn.law.stanford.edu/wp-content/uploads/2020/02/ACUS-AI-Report.pdf [Read 6–13, 22–29, 37–45, 75–78, 82–85] 
• Dillon Reisman et al., Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability, AI NOW (2018), pp. 7-20. https://ainowinstitute.org/aiareport2018.pdf 
• Maria De-Arteaga et al., A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores, ACM Conference On Human Factors In Computing Systems (2020), https://arxiv.org/pdf/2002.08035.pdf 
• Artificial Intelligence in Society, Chapter 4 [Public Policy Considerations], (OECD, 2019)

Supplementary

• “Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?” by Paul Laat (Philosophy & Technology, 2018)

Class 25. Monday, November 28: AI, power, and diversity and inclusion

• “With Great Tech Comes Great Responsibility” by Mozilla Foundation (2020)
• “The American Corporation is in Crisis—Let's Rethink It” by Lenore Palladino (Boston Review, 2019)
• “Discriminating Systems: Gender, Race, and Power in AI” by Sarah West, Meredith Whittaker, and Kate Crawford (AI Now Institute, 2019)
• Groups of diverse problem solvers can outperform groups of high-ability problem solvers (Proceedings of the National Academy of Sciences, 2004)
• Unlocking the Clubhouse: The Carnegie Mellon Experience by Margolis and Fisher

Supplementary:

• Women and Minorities in Tech, By the Numbers by Myers (https://www.wired.com/story/computer-science-graduates-diversity/)
• Another site that has statistics on tech company diversity (in comparison to the US population) is: Diversity in Tech — Information is Beautiful

Class 26. Wednesday, November 30: Student Presentations of Case Studies

Class 27. Monday, December 5: Student Presentations of Case Studies/ Research Papers

Class 28. Wednesday, December 7: Wrap-up and discuss publishing case studies

• Acemoglu. 2021. Dangers of unregulated artificial intelligence. https://voxeu.org/article/dangers-unregulated-artificial-intelligence