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ARXIV:2605.00762 · FAIRNESS IN ML · SUBMITTED 04 MAY · 20:24 UTC · FRESHNESS STALE
ARXIV:2605.00762FAIRNESS IN MLSUBMITTED 04 MAY · 20:24 UTCFRESHNESS STALEShradha Sharma · Swapnil Dhamal · Shweta Jain · arXiv
This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions.
Opportunity summary
Pain This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions. Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback,…
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show that $K$-Shapley value is a unique solution concept that satisfies Symmetry, Linearity, Null player, and efficiency properties. Code availability is flagged in…
Fairness in ML moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions.
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10.48550/arXiv.2605.00762This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions.
Abstract
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly more challenging. To compute arm contributions in BCMAB-FBF, we first extend the Shapley value, a classical solution concept from cooperative game theory, to the $K$-Shapley value, which captures the marginal contribution of an agent restricted to a set of size at most $K$. We show that $K$-Shapley value is a unique solution concept that satisfies Symmetry, Linearity, Null player, and efficiency properties. We next propose K-SVFair-FBF, a fairness-aware bandit algorithm that adaptively estimates $K$-Shapley value with unknown valuation function. Unlike standard bandit literature on full bandit feedback, K-SVFair-FBF not only learns the valuation function under full feedback setting but also mitigates the noise arising from Monte Carlo approximations. Theoretically, we prove that K-SVFair-FBF achieves $O(T^{3/4})$ regret bound on fairness regret. Through experiments on federated learning and social influence maximization datasets, we demonstrate that our approach achieves fairness and performs more effectively than existing baselines.
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PROBLEM
This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions. Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting...
METHOD
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly mo...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show that $K$-Shapley value is a unique solution concept that satisfies Symmetry, Linearity, Null player, and efficiency properties. Code availability is flagged in the production record; the public re...
WHY NOW
Fairness in ML moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions. Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly more challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly more challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show that $K$-Shapley value is a unique solution concept that satisfies Symmetry, Linearity, Null player, and efficiency properties. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Fairness in ML moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper proposes a framework for meritocratic fairness in budgeted combinatorial multi-armed bandits using Shapley values to compute arm contributions.
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Fairness in ML
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4.0/10 public viability
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