Proves coNP-completeness of verifying mPJR in metric clustering, defines mPJR+ with polynomial-time find-and-check algorithms, and introduces DC-mPJR+ with O(mn log n + mnk) verification that approximates mPJR+.
Proceedings of the 3rd innovations in theoretical computer science conference , pages=
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
Introduces a multi-resolution spatial partitioning and scan statistic method to detect unfairness in predictive models based on movement patterns, validated as effective on synthetic datasets.
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.
citing papers explorer
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Check, Please: Verifiably Fair Clustering
Proves coNP-completeness of verifying mPJR in metric clustering, defines mPJR+ with polynomial-time find-and-check algorithms, and introduces DC-mPJR+ with O(mn log n + mnk) verification that approximates mPJR+.
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Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
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Assessing Predictive Models for Fairness Based on Movement Patterns
Introduces a multi-resolution spatial partitioning and scan statistic method to detect unfairness in predictive models based on movement patterns, validated as effective on synthetic datasets.
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Causal Discovery via Statistical Power (CDSP)
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
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Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.