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New research highlights the "selection problem" in Explainable AI

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a significant challenge in Explainable AI (XAI) where users struggle to select the most appropriate explanation technique due to the siloed nature of current XAI interfaces. This "selection problem" arises because users must translate their natural-language uncertainty into a specific technique, a prerequisite that is often difficult to meet. To address this, a proposed solution involves a multi-agent Large Language Model (LLM) orchestration tool designed to automatically translate user queries into the correct XAI explanation technique. AI

IMPACT Addresses a key usability challenge in AI, potentially improving how users interact with and understand AI systems.

RANK_REASON The cluster contains a research paper detailing a problem and proposing a solution within the field of Explainable AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research highlights the "selection problem" in Explainable AI

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The cluster contains a research paper detailing a problem and proposing a solution within the field of Explainable AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Claire Vlases, Katelyn Morrison ·

    Addressing the Selection Problem in Explainable AI

    arXiv:2608.22356v1 Announce Type: new Abstract: Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argue that, given the siloed nature of conventional XAI,…

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