Explainable AI: An Agenda for Explainability Activism
Abstract
are we able to trust it and make the technology accountable? Academic libraries are
evaluating, providing, using, and increasingly building AI-based information tools and
services. Typically, the underlying models for these intelligent systems are large language
models (LLMs) based on generative AI techniques. While many of these systems have
shown remarkable advances and advantages, the risks and deficiencies are also widely
known and easily demonstrated
References
Peter Slattery, Alexander Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel,
Risto Uuk, James Dao, Soroush Pour, Stephen Casper, and Neil Thompson, “The AI Risk
Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial
Intelligence,” ResearchGate, August 2024, https://doi.org/10.13140/RG.2.2.28850.00968.
Tania Lombrozo, “The Structure and Function of Explanations,” Trends in Cognitive
Sciences 10, no. 10 (2006): 464, https://doi.org/10.1016/j.tics.2006.08.004.
Michael Ridley, “Explainable AI (XAI): Adoption and Advocacy,” Information Technology
& Libraries 41, no. 2 (2022): 1–17, https://doi.org/doi.org/10.6017/ital.v41i2.14683.
Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva
Schmidt, Andreas Sesing, and Kevin Baum, “What Do We Want from Explainable Artificial
Intelligence (XAI)?—A Stakeholder Perspective on XAI and a Conceptual Model Guiding
Interdisciplinary XAI Research,” Artificial Intelligence 296 (July 2021): 1, https://doi
.org/10.1016/j.artint.2021.103473.
Edward H. Shortliffe, Stanton G. Axline, Bruce G. Buchanan, Thomas C. Merigan,
and Stanley N. Cohen, “An Artificial Intelligence Program to Advise Physicians Regarding
Antimicrobial Therapy,” Computers and Biomedical Research 6, no. 6 (1973): 544–60, https://
doi.org/10.1016/0010-4809(73)90029-3.
Sajid Ali, Tamer Abuhmed, Shaker El-Sappagh, Khan Muhammad, Jose M. Alonso-
Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz-Rodríguez, and
Francisco Herrera, “Explainable Artificial Intelligence (XAI): What We Know and What Is
Left to Attain Trustworthy Artificial Intelligence,” Information Fusion 99 (2023), https://doi
.org/10.1016/j.inffus.2023.101805.
Tim Miller, Piers Howe, and Liz Sonenberg, “Explainable AI: Beware of Inmates
Running the Asylum” paper, International Joint Conference on Artificial Intelligence, Workshop
on Explainable Artificial Intelligence (XAI), Melbourne, 2017, https://doi.org/10.48550
/arXiv.1712.00547.
Upol Ehsan, Elizabeth Anne Watkins, Philipp Wintersberger, Carina Manger,
Sunnie S. Kim, Niels van Berkel, Andreas Riener, and Mark O. Riedl, “Human-Centered
Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models
(LLMs),” in Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing
Systems, CHI EA ’24 (Association for Computing Machinery, 2024), 2, https://doi.
org/10.1145/3613905.3636311.
Upol Ehsan and Mark O. Riedl, “Human-Centered Explainable AI: Towards a
Reflective Sociotechnical Approach,” in HCI International 2020—Late Breaking Papers:
Multimodality and Intelligence, ed. Constantine Stephanidis et al., Lecture Notes in Computer
Science (Springer International, 2020), 464, https://doi.org/10.1007/978-3-030-
-1_33.
Michael Ridley, “Human-Centered Explainable Artificial Intelligence: An Annual
Review of Information Science and Technology (ARIST) Paper,” Journal of the American
Society for Information Science (2024): 1–23, https://doi.org/10.1002/asi.24889.
Robert R. Hoffman, Timothy Miller, Gary Klein, Shane T. Mueller, and William J.
Clancey, “Increasing the Value of XAI for Users: A Psychological Perspective,” KI—Künstliche
Intelligenz (2023), https://doi.org/10.1007/s13218-023-00806-9.
Michael Ridley, “Protocols Not Platforms: The Case for Human-Centered Explainable
AI (HCXAI),” 2023, https://cais2023.ca/talk/10.ridley/10.Ridley.pdf.
Mike Masnick, “Protocols, Not Platforms: A Technological Approach to Free Speech,”
Knight First Amendment Institute at Columbia University, August 21, 2019, p. 6, https://
knightcolumbia.org/content/protocols-not-platforms-a-technological-approach-to-free
-speech.
Luca Nannini, Agathe Balayn, and Adam Leon Smith, “Explainability in AI Policies:
A Critical Review of Communications, Reports, Regulations, and Standards in the
EU, US, and UK,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability,
and Transparency, FAccT ’23 (Association for Computing Machinery, 2023), 1198–1212,
https://doi.org/10.1145/3593013.3594074.2023
Latanya Sweeney, “How to Save Democracy and the World,” paper, ACM Conference
on Fairness, Accountability, and Transparency, New York University, 2018.
Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us (MIT
Press, 2024).
Article Views (By Year/Month)
| 2026 |
| January: 157 |
| 2025 |
| January: 0 |
| February: 0 |
| March: 0 |
| April: 1306 |
| May: 593 |
| June: 244 |
| July: 224 |
| August: 196 |
| September: 148 |
| October: 151 |
| November: 185 |
| December: 203 |