Actionable Approaches to Promote Ethical AI in Libraries
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Abstract (arXiv)
The widespread use of artificial intelligence (AI) in many domains has revealed numerous ethical issues from data and design to deployment. In response, countless broad principles and guidelines for ethical AI have been published, and following those, specific approaches have been proposed for how to encourage ethical outcomes of AI. Meanwhile, library and information services too are seeing an increase in the use of AI-powered and machine learning-powered information systems, but no practical guidance currently exists for libraries to plan for, evaluate, or audit the ethics of intended or deployed AI. We therefore report on several promising approaches for promoting ethical AI that can be adapted from other contexts to AI-powered information services and in different stages of the software lifecycle.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 20 Sep 2021.
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References · 6
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
- 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
- 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
- 2021AI Certification: Advancing Ethical Practice by Reducing Information Asymmetriescited by 15
- 2019Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviourcited by 6
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