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Below is what we are reading in class according to some of the themes and questions the ECSE681 students raised as their priority interests for the course this semester. Feel free to read along at your own pace.

AI Governance and Audits -- Part 1

What is expected in an audit?

  • Schiff, D. S., Kelley, S., & Camacho Ibáñez, J. (2024). The emergence of artificial intelligence ethics auditing. Big Data & Society, 11(4), 20539517241299732. https://doi.org/10.1177/20539517241299732

AI ethics principles, charters, manifestos

  • Gornet, M., Delarue, S., Boritchev, M., & Viard, T. (2024). Mapping AI ethics: A meso-scale analysis of its charters and manifestos. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 127–140. https://doi.org/10.1145/3630106.3658545

  • Blue, G., & Hogan, M. (2024). Getting democracy wrong: How lessons from biotechnology can illuminate limits of the Asilomar AI principles. Journal of Digital Social Research, 6(4), Article 4. https://doi.org/10.33621/jdsr.v6i440477

  • Tidjon, L. N., & Khomh, F. (2022). The Different Faces of AI Ethics Across the World: A Principle-To-Practice Gap Analysis. IEEE Transactions on Artificial Intelligence, 1–20. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2022.3225132

AI governance and power dynamics

  • Stein, M., Gandhi, M., Kriecherbauer, T., Oueslati, A., & Trager, R. (2024). Public vs Private Bodies: Who Should Run Advanced AI Evaluations and Audits? A Three-Step Logic Based on Case Studies of High-Risk Industries. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 1401–1415. https://doi.org/10.1609/aies.v7i1.31733](https://doi.org/10.1609/aies.v7i1.31733)

  • Bogiatzis-Gibbons, D. J. (2024). Beyond Individual Accountability: (Re-)Asserting Democratic Control of AI. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 74–84. https://doi.org/10.1145/3630106.3658541

Effectiveness of audits

  • Terzis, P., Veale, M., & Gaumann, N. (2024). Law and the Emerging Political Economy of Algorithmic Audits. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 1255–1267. https://doi.org/10.1145/3630106.3658970

  • Sterz, S., Baum, K., Biewer, S., Hermanns, H., Lauber-Rönsberg, A., Meinel, P., & Langer, M. (2024). On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 2495–2507. https://doi.org/10.1145/3630106.3659051

  • Casper, S., Ezell, C., Siegmann, C., Kolt, N., Curtis, T. L., Bucknall, B., Haupt, A., Wei, K., Scheurer, J., Hobbhahn, M., Sharkey, L., Krishna, S., Von Hagen, M., Alberti, S., Chan, A., Sun, Q., Gerovitch, M., Bau, D., Tegmark, M., … Hadfield-Menell, D. (2024). Black-Box Access is Insufficient for Rigorous AI Audits. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 2254–2272. https://doi.org/10.1145/3630106.3659037

  • Hasan, A., Brown, S., Davidovic, J., Lange, B., & Regan, M. (2022). Algorithmic Bias and Risk Assessments: Lessons from Practice. Digital Society, 1(2), 14. https://doi.org/10.1007/s44206-022-00017-z

Alternative modes of governance

  • Carpenter, D., & Ezell, C. (2024). An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 239–254. https://doi.org/10.1609/aies.v7i1.31633

  • Bommasani, R., Klyman, K., Longpre, S., Xiong, B., Kapoor, S., Maslej, N., Narayanan, A., & Liang, P. (2024). Foundation Model Transparency Reports. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 181–195. https://doi.org/10.1609/aies.v7i1.31628

Clarifying terminologies

  • Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024). AI auditing: The Broken Bus on the Road to AI Accountability (No. arXiv:2401.14462). arXiv. https://doi.org/10.48550/arXiv.2401.14462

  • Autumn Toney-Wails, Kathleen Curlee, and Emelia Probasco. 2024. Trust Issues: Discrepancies in Trustworthy AI Keywords Use in Policy and Research. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24), June 03–06, 2024, Rio de Janeiro, Brazil. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3630106.3659035

A deeper dive with a case study

  • Mahomed, Y., Crawford, C. M., Gautam, S., Friedler, S. A., & Metaxa, D. (2024). Auditing GPT’s Content Moderation Guardrails: Can ChatGPT Write Your Favorite TV Show? The 2024 ACM Conference on Fairness, Accountability, and Transparency, 660–686. https://doi.org/10.1145/3630106.3658932

  • Jaiswal, S., Ganai, A., Dash, A., Ghosh, S., & Mukherjee, A. (2024). Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 634–646. https://doi.org/10.1609/aies.v7i1.31666

How do you audit tech that is so new?

  • Feffer, M., Sinha, A., Deng, W. H., Lipton, Z. C., & Heidari, H. (2024). Red-Teaming for Generative AI: Silver Bullet or Security Theater? Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 421–437. https://doi.org/10.1609/aies.v7i1.31647

  • Cheong, I., Xia, K., Feng, K. J. K., Chen, Q. Z., & Zhang, A. X. (2024). (A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 2454–2469. https://doi.org/10.1145/3630106.3659048

AI governance in the Global North vs. South

  • Lehdonvirta, V., Wú, B., & Hawkins, Z. (2024). Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 828–838. https://doi.org/10.1609/aies.v7i1.31683

  • Jaiswal, S., Ganai, A., Dash, A., Ghosh, S., & Mukherjee, A. (2024). Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 634–646. https://doi.org/10.1609/aies.v7i1.31666

AI Governance and Audits -- Part 2

Evaluating AI as a system

  • Gorwa, R., & Veale, M. (2023, November 17). Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries. https://doi.org/10.31235/osf.io/6dfk3

  • Rismani, S., Shelby, R., Smart, A., Delos Santos, R., Moon, Aj., & Rostamzadeh, N. (2023). Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 70–83. https://doi.org/10.1145/3600211.3604685

A deeper dive with a case study

  • Groves, L., Metcalf, J., Kennedy, A., Vecchione, B., & Strait, A. (2024). Auditing Work: Exploring the New York City algorithmic bias audit regime. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 1107–1120. https://doi.org/10.1145/3630106.3658959

  • Kieslich, K., & Lünich, M. (2024). Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 173–185. https://doi.org/10.1145/3630106.3658548

Inspirations from security

  • Cattell, S., Ghosh, A., & Kaffee, L.-A. (2024). Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 267–280. https://doi.org/10.1609/aies.v7i1.31635

  • Iqbal, U., Kohno, T., & Roesner, F. (2024). LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI’s ChatGPT Plugins. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 611–623. https://doi.org/10.1609/aies.v7i1.31664

Failures/abandonment/repair

  • Johnson, N., Moharana, S., Harrington, C., Andalibi, N., Heidari, H., & Eslami, M. (2024). The Fall of an Algorithm: Characterizing the Dynamics Toward Abandonment. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 337–358. https://doi.org/10.1145/3630106.3658910

  • Schwennesen, N. (2019). Algorithmic assemblages of care: Imaginaries, epistemologies and repair work. Sociology of Health & Illness, 41(S1), 176–192. https://doi.org/10.1111/1467-9566.12900

How do we handle tech that is so new?

  • Klyman, K. (2024). Acceptable Use Policies for Foundation Models. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 752–767. https://doi.org/10.1609/aies.v7i1.31677

Research ethics and AI

  • Nanayakkara, P., Hullman, J., & Diakopoulos, N. (2021). Unpacking the Expressed Consequences of AI Research in Broader Impact Statements. AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 1. https://doi.org/10.1145/3461702.3462608

  • Cha, I., Pillai, A. G., & Wong, R. Y. (2024). Ethics Pathways: A Design Activity for Reflecting on Ethics Engagement in HCI Research. Proceedings of the 2024 ACM Designing Interactive Systems Conference, 3515–3533. https://doi.org/10.1145/3643834.3660714

Who gets credit for what AI generates?

  • Goetze, T. S. (2024). AI Art is Theft: Labour, Extraction, and Exploitation: Or, On the Dangers of Stochastic Pollocks. The 2024 ACM Conference on Fairness, Accountability, and Transparency, 186–196. https://doi.org/10.1145/3630106.3658898

  • Episode 26: Who owns AI-generated creations (and why you should care). (2023, March 1). Innovation, Science and Economic Development Canada. https://ised-isde.canada.ca/site/canadian-intellectual-property-office/en/episode-26-who-owns-ai-generated-creations-and-why-you-should-care