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Beyond XAI:Obstacles Towards Responsible AI

arXiv 2309.03638 ↗First posted 7 Sep 2023cs.AIcited by 0

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Abstract (arXiv)

The rapidly advancing domain of Explainable Artificial Intelligence (XAI) has sparked significant interests in developing techniques to make AI systems more transparent and understandable. Nevertheless, in real-world contexts, the methods of explainability and their evaluation strategies present numerous limitations.Moreover, the scope of responsible AI extends beyond just explainability. In this paper, we explore these limitations and discuss their implications in a boarder context of responsible AI when considering other important aspects, including privacy, fairness and contestability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 44 references that are in the map. First posted on arXiv 7 Sep 2023.

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References · 23

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11 more
  1. 2018RuleMatrix: Visualizing and Understanding Classifiers with Rulescited by 62
  2. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  3. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  4. 2022Challenges in Applying Explainability Methods to Improve the Fairness of NLP Modelscited by 20
  5. 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
  6. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
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  8. 2022Towards Involving End-users in Interactive Human-in-the-loop AI Fairnesscited by 12
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  10. 2022Privacy Meets Explainability: A Comprehensive Impact Benchmarkcited by 9
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