提出抗攻击公平性算法，保障推荐系统长期公平与高效。
Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits
- 设计新算法实现线性与平滑奖励下的近最优后悔界。
- 在对抗攻击下仍保持接近1的公平性保证（随时间趋近1）。
- 适合关注平台公平性与鲁棒性的推荐系统研究者。
现代数字平台和服务系统广泛采用上下文老虎机进行在线决策，但其部署可能无意中导致各选项间曝光不公平，损害平台长期可持续性和供应商信任。本文研究在统一(1−δ)-公平约束下的上下文老虎机问题，揭示其对策略性操纵的独特脆弱性。公平约束通过一致性机制确保优先待遇仅由实际回报决定，防止统计漏洞。我们提出新算法，在线性与平滑奖励函数下实现（近）最小最大最优后悔，同时维持强(1−Õ(1/T))-公平性保障，并刻画了理论上固有但渐近可忽略的“公平代价”。然而，我们发现此类基于绩效的公平性极易受信号操纵。实验表明，拥有极小Õ(1)预算的对手不仅降低整体性能，还可选择性引发隐蔽的公平性失效，而明显后悔指标几乎不受影响。为此，我们设计鲁棒变体，引入适应性探索与误差补偿阈值。该方法首次在C-预算攻击下实现最小最大最优后悔，同时保持(1−Õ(1/T))-公平性。数值实验与真实案例验证了算法在公平性与效率上的持续性。
原文摘要 · Abstract (English)
Modern systems, such as digital platforms and service systems, increasingly rely on contextual bandits for online decision-making; however, their deployment can inadvertently create unfair exposure among arms, undermining long-term platform sustainability and supplier trust. This paper studies the contextual bandit problem under a uniform $(1-δ)$-fairness constraint, and addresses its unique vulnerabilities to strategic manipulation. The fairness constraint ensures that preferential treatment is strictly justified by an arm's actual reward across all contexts and time horizons, using uniformity to prevent statistical loopholes. We develop novel algorithms that achieve (nearly) minimax-optimal regret for both linear and smooth reward functions, while maintaining strong $(1-\tilde{O}(1/T))$-fairness guarantees, and further characterize the theoretically inherent yet asymptotically marginal "price of fairness". However, we reveal that such merit-based fairness becomes uniquely susceptible to signal manipulation. We show that an adversary with a minimal $\tilde{O}(1)$ budget can not only degrade overall performance as in traditional attacks, but also selectively induce insidious fairness-specific failures while leaving conspicuous regret measures largely unaffected. To counter this, we design robust variants incorporating corruption-adaptive exploration and error-compensated thresholding. Our approach yields the first minimax-optimal regret bounds under $C$-budgeted attack while preserving $(1-\tilde{O}(1/T))$-fairness. Numerical experiments and a real-world case demonstrate that our algorithms sustain both fairness and efficiency.
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