Bayesian Fairness

Fairness, Computable Fairness, and Randomness

Analysing the Fairness of Fairness-aware Classifiers

Calders and Verwer’s two-naive-Bayes is one of fairness-aware classifiers, which classify objects while excluding the influence of a specific information. We analyze why this classifier achieves very high level of the fairness, and show that this is due to a decision rules and a model bias. Based on these findings, we develop methods that are grounded on rigid theory and are applicable to wider...

Correction: Expanded HTA: Enhancing Fairness and Legitimacy

Expanded HTA: Enhancing Fairness and Legitimacy

All societies face the need to make judgments about what interventions (both public health and personal medical) to provide to their populations under reasonable resource constraints. Their decisions should be informed by good evidence and arguments from health technology assessment (HTA). But if HTA restricts itself to evaluations of safety, efficacy, and cost-effectiveness, it risks being vie...

Defining Fairness

We propose a definition for the class of all fairness properties of a given system. We provide independent characterizations in terms of topology, language theory and game theory. All popular notions of fairness from the literature satisfy our definition. Moreover our class is closed under union and countable intersection, and it is, in a sense, the maximal class having this property. On the wa...