AGENTIC-SYSTEMSApril 23, 20261 min read7 views

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

This paper discusses how integrating learning theories into the XAI lifecycle can enhance human agency and mitigate risks by adopting a learner-centered approach to AI explanations.

Computer Science > Artificial Intelligence

Title:Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

View PDF HTML (experimental)Abstract:As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI.

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Source: arXiv cs.AI Recent

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