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  3. Rules or Weights? Comparing User Understanding of Explainabl
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Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model

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Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: stale

Source paper: Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model

PDF: https://arxiv.org/pdf/2602.19620v1

Source count: 0

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

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Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model

Overall score: 4/10
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Canonical Paper Receipt

Last verification: 2026-03-19T18:48:05.835Z

Freshness: stale

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References: 0

Sources: 0

Coverage: 33%

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Builds On This
Exploring SAIG Methods for an Objective Evaluation of XAI
Score 2.0down
Prior Work
Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
Score 4.0stable
Higher Viability
GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks
Score 7.0up
Higher Viability
XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights
Score 8.0up
Higher Viability
Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations
Score 5.0up
Higher Viability
Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer
Score 5.0up
Competing Approach
Position: Explainable AI is Causality in Disguise
Score 2.0down
Competing Approach
Position: Explaining Behavioral Shifts in Large Language Models Requires a Comparative Approach
Score 2.0down

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Related Resources

  • explainable AI (XAI)(glossary)
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  • How can LLM adaptation help in building more explainable AI systems?(question)
  • How can vision-language models be leveraged for explainable AI in visual question answering?(question)

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