Michael Ridley

Ridley, M. (2025). Explainable AI: An Agenda for Explainability Activism. College & Research Libraries News, 86(4), Article 4. https://doi.org/10.5860/crln.86.4.169

INTRODUCTION

If artificial intelligence (AI), particularly generative AI, is an opaque “black box,” how 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.1 One path to trust and accountability is through explainability: the capacity of AI tools and services to explain their outcomes, recommendations, or decisions. Academic libraries need to adopt a multifaceted approach to explainability to ensure intelligent systems meet (and hopefully exceed) our expectations for authority, credibility, and accuracy.

Ridley, M. (2024, November 6). Looking backwards to see ahead: The case of expert systems development in libraries.  Information Matters, 4 (11). https://dx.doi.org/10.2139/ssrn.5024825

ABSTRACT

This short piece recommends looking at expert system development in libraries during the 1980s & 1990s as a way to advance current AI/ML developments. It is based on the paper in Library & Information History 40.1 (2024): 46–67 

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Ridley, Michael (2024). Informing Algorithmic Literacy Through User Folk Theories. College and Research Libraries, 85(7), 1-12. 

ABSTRACT

As part of a broader information literacy agenda, academic libraries are interested in advancing algorithmic literacy. Folk theories of algorithmic decision-making systems, such as recommender systems, can provide insights into designing and delivering enhanced algorithmic literacy initiatives. Users of the Spotify music recommendation systems were surveyed and interviewed to elicit their folk theories about how music recommendations are made. Seven folk theories emerged from this study and are grouped into four themes: agency, context, trust, and feelings. These four themes are used to illustrate how folk theories can inform algorithmic literacy programming and curricula.

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Ridley, Michael. (2024). Explainable AI: Implications for Libraries and Archives. Future of Archives and Libraries: How Technology and AI are (Re)Shaping Heritage Institutions. Library and Archives Canada, Ottawa, September 5, 2024.

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Ridley, Michael (2024, April 25). The explainability imperative. Information Matters, 4(4). https://informationmatters.org/2024/04/the-explainability-imperative/
 

ABSTRACT

This short piece responds to implications of the question: If artificial intelligence is so smart, why can't it explain itself?

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Ridley, Michael (2024). Human‐centered explainable artificial intelligence: An Annual Review of Information Science and Technology (ARIST) paper. Journal Of The American Society For Information Science, 1–23. https://doi.org/10.1002/asi.24889
 

ABSTRACT

Explainability is central to trust and accountability in AI applications. The field of human-centered explainable AI (HCXAI) arose as a response to mainstream explainable AI (XAI) which was focused on algorithmic perspectives and technical challenges, and less on the needs and contexts of the non expert, lay user.  HCXAI is characterized by putting humans at the center of AI explainability. Taking a sociotechnical perspective, HCXAI prioritizes user and situational contexts, preferences reflection over acquiescence, and promotes the actionability of explanations. This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field.

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Ridley, Michael (2024). Prototyping expert systems in reference services (1980–2000):  experimentation, success, disillusionment, and legacy. Library & Information History 40.1 (2024): 46–67 https://doi.org/10.3366/lih.2024.0165

ABSTRACT

In the late twentieth century librarians prototyped expert systems in reference services in order to respond to the reference ‘crisis’ of the time and to harness the power of emerging artificial intelligence (AI) technologies. Creating intelligent systems required librarian designers to codify the expertise of reference librarians and the resources of reference services into knowledge representation mechanisms suitable for inferences by the system. In this process, librarians explored the theoretical and pragmatic bases of reference and experimented with how to implement them in AI. The successes and failures of these prototypes reveal how librarians felt about these new technologies and how they might transform libraries. While expert systems in libraries failed and were abandoned by the end of the century, lessons and insights from this seminal work can inform current activities in the application of machine learning.