aiqbench analysis
This research addresses a critical gap in explainable AI by formalizing how to select among multiple competing explanations generated under uncertainty. The proposed framework enables applications to explicitly prioritize explanations based on specific constraints like prediction confidence or decision boundaries, moving beyond generic generation to context-aware selection. By validating across 41 datasets, the study demonstrates that making selection policies explicit significantly reduces disagreement rates compared to standard confidence-based approaches.
When Explanations Compete: Policy-Aware Selection Under Uncertainty
arXiv:2410.05479v2 Announce Type: replace-cross Abstract: Uncertainty-aware explanation methods often produce several alternatives for the same prediction. Selecting among them requires a policy for balancing prediction confidence, uncertainty, and application constraints. This paper presents a framework for applying such policies to a fixed set of generated explanations. Candidates are characterised by uncertainty change, prediction direction, and, when available, interval position relative to a decision boundary. The framework combines these properties with eligibility rules, optional bidirectional Pareto screening, and policy-aware ranking. A fictitious prostate-cancer example illustrates how different explanatory purposes lead to different selections from the same candidate set. We instantiate the framework with Calibrated Explanations for classification, thresholded regression, and plain regression. Across 41 benchmark datasets, mean candidate counts range from 11.57 to 21.75 for single-feature explanations and from $29.48$ to $69.53$ when conjunctions are included. Equal-weight and confidence-only policies yield an average selection-disagreement rate of $28.7\%$ while favouring the same confidence direction. A supporting $\delta$-CLUE experiment demonstrates use with a second generator. By making the selection policy explicit, the framework allows applications to compare and prioritise explanations according to their intended use.
Abstract:Uncertainty-aware explanation methods often produce several alternatives for the same prediction. Selecting among them requires a policy for balancing prediction confidence, uncertainty, and application constraints. This paper presents a framework for applying such policies to a fixed set of generated explanations. Candidates are characterised by uncertainty change, prediction direction, and, when available, interval position relative to a decision boundary. The framework combines these properties with eligibility rules, optional bidirectional Pareto screening, and policy-aware ranking. A fictitious prostate-cancer example illustrates how different explanatory purposes lead to different selections from the same candidate set. We instantiate the framework with Calibrated Explanations for classification, thresholded regression, and plain regression. Across 41 benchmark datasets, mean candidate counts range from 11.57 to 21.75 for single-feature explanations and from $29.48$ to $69.53$ when conjunctions are included. Equal-weight and confidence-only policies yield an average selection-disagreement rate of $28.7\%$ while favouring the same confidence direction. A supporting $\delta$-CLUE experiment demonstrates use with a second generator. By making the selection policy explicit, the framework allows applications to compare and prioritise explanations according to their intended use.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2410.05479 [cs.AI]
(or arXiv:2410.05479v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2410.05479 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 272 words
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