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Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik, David Pape
https://arxiv.org/abs/2506.00496

Monitoring Robustness and Individual Fairness
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past…

This https://arxiv.org/abs/2402.08533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the above questions are positive. Therefore, when allocating resources to different customers, sellers should consider both the total reward and in…