Government regulations, EOs, and the like:
- White House, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, October 30, 2023.
- State of California, Draft Automated Decisionmaking Technology Regulations, December 2023.
Government resources:
- Administrative Conference of the United States, https://www.acus.gov/research-projects/algorithmic-tools-retrospective-review-agency-rules.
- GSA, FY 23 Governmentwide Section 508 Assessment.
- NIST Resource Management Framework.
- Securities and Exchange Commission, Strategic Hub for Innovation and Financial Technology (FinHub); material on AI at the bottom.
- Introducing the Bias Toolkit. Toolkit to help understand and mitigate bias in data and algorithms.
- Benefits Tech Advocacy Hub: Resource List.
Other recommended resources:
Resources from our meeting (slides, etc.):
Legal and Policy Background Papers:
- Citron, Danielle Keats, Technological Due Process, 85 Wash. U. L. Rev. 1249 (2008).
- Engstrom, David Freeman, Ho, Daniel E., Cuéllar, Mariano-Florentino, Sharkey, Catherine M., Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, 2020 (for background on thinking about AI/ML use in government agencies).
- Wexler, Rebecca, Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, 70 Stanford Law Review 1343 (2018).
Technical papers as background:
- Jenny L. Davis, Apryl Williams, and Michael W. Yang, Algorithmic reparation, Big Data and Society, 2021. (This discusses contestability affordances in AI being important for algorithmic reparation/redress; note: it doesn't use the word "contestability.")
- Henrietta Lyons, Eduardo Velloso, and Tim Miller, Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions, ACM Hum.-Comput. Interact., Vol. 5, No. CSCW1, 2021.
- Brent Mittelstadt, Chris Russell, and Sandra Wachter, Explaining Explanations in AI, FAT 19, 2019.
- Cynthia Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence, 2019.
- Aler Tubella, A., Theodorou, A., Dignum, V., et al. (2020). Contestable black boxes. In V. Gutiérrez-Basulto, T. Kliegr, A. Soylu, et al. (Eds.), Rules and reasoning (Vol. 12173). Springer.
- Ehsan Upol, Q. Vera Liao, Samir Passi, Mark O. Riedl, Hal Daume III, Seamful XAI: Operationalizing Seamful Design in Explainable AI.
- Berk Usten, Alexander Spangher, Yang Liu, Actionable Recourse in Linear Classification.