Inclusive and Explainable AI Systems: A Systematic Literature Review
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2024-01-03
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1297
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Abstract
Explainable AI (XAI) plays a crucial role in enhancing transparency and providing rational explanations to support users of AI systems. Inclusive AI actively seeks to engage and represent individuals with diverse attributes who are affected by and contribute to the AI ecosystem. Both inclusion and XAI advocate for the active involvement of the users and stakeholders during the entire AI systems lifecycle. However, the relationship between XAI and Inclusive AI has not been explored. In this paper, We present the results of a systematic literature review with the objective to explore this relationship in the recent AI reserach literature. We were able to identify 18 research articles on the topic. Our analysis focused on exploring approaches to: (1) the humans attributes and perspectives, (2) preferred explanation methods, and (3) Human-AI interaction. Based on our findings, we identified potential future XAI research directions and proposed strategies for practitioners involved in the design and development of inclusive AI systems.
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Explainable Artificial Intelligence (XAI), explainable ai, human-centered, inclusion, transparency
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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