Check out this Q&A for more information about this semi-regular, interdisciplinary reading group.

Thanks to Peter A. Allard School of Law and Professor Cristie Ford for supporting this group.

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• Kathleen Creel & Deborah Hellman, "The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems" (2021) ACM Conference on Fairness, Accountability, and Transparency, online: <papers.ssrn.com/sol3/papers.cfm?abstract_id=3786377>.

• Ryan Calo & Danielle Keats Citron, "The Automated Administrative State: A Crisis of Legitimacy", Emory Law Journal [forthcoming in 2020], online: <papers.ssrn.com/sol3/papers.cfm?abstract_id=3553590>.

• Nenad Tomašev et al, "AI for Social Good: Unlocking the Opportunity for Positive Impact", (18 May 2020) 11 Nature Communications, online: <doi.org/10.1038/s41467-020-15871-z>. (With guest discussion leads: Nenad Tomašev and Shakir Mohamed, two of this paper's co-authors.)

• Sandra Wachter, Brent Mittelstadt & Chris Russell, "Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR" (2018) 31:2 Harvard J Law & Tech 841.

• Discussion of Anya Prince & Daniel Schwarcz, "Proxy Discrimination in the Age of Artificial Intelligence and Big Data" (forthcoming in 2020) Iowa LR.

• Introductory session. Discussed general terminology, assumptions, and goals of algorithmic/AI decision-making. Discussion was based around Hildebrandt's Chapter 2, Wachter, Mittelstadt & Russell's "Counterfactual Explanations", Molnar and Gil's Bots at the Gate, and the Treasury Board's Directive on Automated Decision-Making.

• Mireille Hildebrandt, Law for Computer Scientists (Oxford University Press, 2019).
• Anya Prince & Daniel Schwarcz, "Proxy Discrimination in the Age of Artificial Intelligence and Big Data" (forthcoming in 2020) Iowa LR.
• John Zerilli et al, "Algorithmic Decision-Making and the Control Problem" (2019) 29:4 Minds & Machines 555.
• Karen Yeung & Margin Lodge, eds, Algorithmic Regulation (Oxford University Press, 2019).
• Amanda Levendowski, "How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem" (2018) 93:2 Wash L Rev 579.
• Mary Liston, "Expanding the Parameters of Participatory Public Law: A Democratic Right to Public Participation and the State's Duty of Public Consultation" (2017) 63:2 McGill LJ 375.
• Deirdre K Mulligan & Kenneth A Bamberger, "Procurement as Policy: Administrative Process for Machine Learning" (2019) 34 Berkeley Tech LJ 773
• Ryan Calo & Danielle Keats Citron, "The Automated Administrative State: A Crisis of Legitimacy", Emory Law Journal [forthcoming in 2020], online: <papers.ssrn.com/sol3/papers.cfm?abstract_id=3553590>.

• Petra Molnar & Lex Gill, Bots at the Gate: A Human Rights Analysis of Automated Decision-Making in Canada's Immigration and Refugee System (Toronto: International Human Rights Program and Citizen Lab, 2018).
• Jennifer Raso, "Unity in the Eye of the Beholder? Reasons for Decision in Theory and Practice in the Ontario Works Program" (2020) 70:1 UTLJ 1
• Andrea Roth, "Machine Testimony" (2017) 126:7 Yale LJ 1972.
• Law Commission of Ontario, The Rise and Fall of AI and Algorithms in American Criminal Justice: Lessons for Canada (Toronto, October 2020).

• Nenad Tomašev et al, "AI for Social Good: Unlocking the Opportunity for Positive Impact", (18 May 2020) 11 Nature Communications, online: <doi.org/10.1038/s41467-020-15871-z>.
• Thoughtworks, "Technology Radar (Vol 21)" (20 November 2019), online: <assets.thoughtworks.com/assets/technology-radar-vol-21-en.pdf>.
• University of Montréal, Montreal Declaration for a Responsible Development of Artificial Intellgence (2017).
• Amnesty International & Access Now, The Toronto Declaration: Protecting the Right to Equality in Machine Learning (2018).
• Mowat Centre, "Governing the Future: Creating Standards for Artificial Intelligence and Algorithms" (3 June 2019).
• Sandra Wachter, Brent Mittelstadt & Chris Russell, "Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR" (2018) 31:2 Harvard J Law & Tech 841.
• Google Pair, "What-If Tool", online: <pair-code.github.io/what-if-tool>.
• Google, "Explainable AI", online: <cloud.google.com/explainable-ai/>.
• Cynthia Rudin & Joanna Radin, "Why Are We Using Black Box Models in AI When We Don't Need To? A Lesson From An Explainable AI Competition" (2019) 1:2 Harvard Data Science Review, DOI: <10.1162/99608f92.5a8a3a3d>.
• Cynthia Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" (2019) 1 Nature Machine Intelligence 206, DOI: <10.1038/s42256-019-0048-x>.
• DARPA, "Explainable AI Program Update" (November 2017), online: <www.darpa.mil/attachments/XAIProgramUpdate.pdf>.
• MIT, "Computational Law Report" (2019), online: <law.mit.edu>.
• Crawford et al, AI Now 2019 Report (New York: AI Now Institute, 2019), online: <ainowinstitute.org/AI_Now_2019_Report.html> (annual report from an institute focused on the social implications of AI technology; recommendations for government and industry relating to workers rights, privacy, decision-making, and accountability).
• Harrison Edwards & Amos Storkey, "Censoring Representations with an Adversary" (2015) International Conference on Learning Representations, online: <arXiv/1511.05897>.

• Canada, Treasury Board, Directive on Automated Decision-Making (Ottawa: Treasury Board, 2019), online: <www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592>.
• EC, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] OJ, L 119/1, art 22.
• Sandra Wachter, Brent Mittelstadt & Luciano Floridi, "Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation" (2017) 7:2 Intl Data Privacy Law 76, online: <academic.oup.com/idpl/article/7/2/76/3860948>.
• Sandra Wachter, Brent Mittelstadt & Chris Russel, "Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI" (2020), online: <arxiv.org/abs/2005.05906>
• Bryce Goodman & Seth Flaxman, "European Union Regulations on Algorithmic Decision-Making and a 'Right to Explanation'".
• EC, Panel for the Future of Science and Technology, "Understanding Algorithmic Decision-Making: Opportunities and Challenges" by Claude Castelluccia & Daniel Le Métayer (Brussels: March 2019).
• EC, Panel for the Future of Science and Technology, "A Governance Framework for Algorithmic Accountability and Transparency" by Ansgar Koene et al (Brussels: April 2019).
• EC, "On Artificial Intelligence -A European approach to excellence and trust" (White Paper), online: <ec.europa.eu/info/files/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en>
• EC, Proposal for a Regulation of the European Parliament and of the Council: Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, OJ 2021/0106/COD, online: <eur-lex.europa.eu/procedure/FI/2021_106>.
• Law Commission of Ontario, Regulating AI: Critical Issues and Choices (Toronto, April 2021).
• Lorna McGregor, Daragh Murray & Vivian Ng, "International Human Rights Law as a Framework for Algorithmic Accountability" (2019) 68:2 Int'l & Comparative LQ.
• Teresa Scassa, "Administrative Law and the Governance of Automated Decision-Making: A Critical Look at Canada's Directive on Automated Decision-Making" (2021) 54:1 UBC L Rev, online (preprint): <papers.ssrn.com/sol3/papers.cfm?abstract_id=3722192>.
• US, Executive Office of the President, Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights (Washington, DC: US Government Printing Office, 2016).
• UK, Royal Academy of Engineering, Algorithms in Decision-Making (Response to House of Commons Science and Technology Committee Inquiry into the Use of Algorithmis in Decision-Making) (April 2017).

• Canada (Minister of Citizenship and Immigration) v Vavilov, 2019 SCC 65 (framework for judicial, substantive review of administrative action).
• Fraser v Canada (Attorney General), 2020 SCC 28 (disparate-impact / adverse-effects claims under s. 15 of the Charter)

• Hima Lakkaraju, "If you have less than 3 hours to spare & want to learn (almost) everything about state-of-the-art explainable [machine learning], this thread is for you!..." (7 May 2021), online: Twitter <twitter.com>.
• Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (New York: Yale University Press, 2021).
• Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (New York: New York University Press, 2018).
• Michael Kearns & Aaron Roth, The Ethical Algorithm: The Science of Socially Aware Algorithm Design (New York, Oxford University Press: 2019); Ipse Dixit, "Aaron Roth & Michael Kearns on Ethical Algorithms" (podcast) (2 March 2019), online: <shows.acast.com/ipse-dixit/episodes/aaron-roth-michael-kearns-on-ethical-algorithms>.
• Florian Martin-Bariteau & Teresa Scassa, Artificial Intelligence and the Law in Canada (Toronto: LexisNexis, 2021) (available open access in 2022).
• Frake Pasquale, The Black Box Society: The Secret Algorithms that Control Money and Information (Cambridge: Harvard University Press, 2015).