Feb 16, 2023 · We propose an individual fairness measure and a corresponding algorithm that deal with the challenges of uncertainty arising from censorship in class labels.
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Moreover, this work defines individual fairness from a ranking perspective, relaxing from the Lipschitz condition in conventional individual fairness studies.
Aug 22, 2024 · This paper delves into the concept of individual fairness amidst censorship and also with group awareness.
Specifically, individual fairness seeks to ensure similar outcomes for similar individuals [13, 49] , while group fairness focuses on equalizing outcome ...
Oct 13, 2024 · These fairness measures are limited in that they utilize point predictions, neglecting their variances, or uncertainties, making them ...
This paper delves into the concept of individual fairness amidst censorship and also with group awareness. We argue that this setup provides a more realistic ...
Fairness has emerged as an important consideration in algorithmic decision making. Unfairness occurs when an agent with higher merit obtains a worse outcome ...
Merits conditioned on observable features are always uncertain, a fact that is exacerbated by the widespread use of machine learning algorithms to infer merit ...
In other words, using a more fine-grained fairness potentially results in lower system and individual performance. Comparison of Fairness Criteria. Table 1 ...
Specifically, the individual fairness axiom states that two individuals similar with respect to a task should receive similar outcomes (Dwork et al., 2012).