When Explanations Compete: Policy-Aware Selection Under Uncertainty
Quick summary
arXiv:2410.05479v2 Announce Type: replace 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 bidirectiona
Key takeaways
- arXiv:2410.05479v2 Announce Type: replace 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “When Explanations Compete: Policy-Aware Selection Under Uncertainty” may reshape data collection, model training, output accountability and market access.
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