Year: 2019
Type: conference-paper
Abstract: A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a calibrated non-binary "scoring" classifier, and then to post-process this score to obtain a binary decision. We study various fairness (or, error-balance) properties of this methodology, when the non-binary scores are calibrated over all protected groups, and with a variety of post-processing algorithms. Specifically, we show:
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
Awards: ATD: Algorithms for Anomaly Detection Using Graphical Models, TWC: TTP Option: Frontier: Collaborative: MACS: A Modular Approach to Cloud Security, SaTC: CORE: Medium: Towards Mechanized Proofs of Composable Security Properties, AF: Small: Boolean Functions: Inequalities, Structure, Algorithms & Hardness, G3627863932, EAGER: Testing Pseudorandom Distributions, G4314282881, AF: Medium: Collaborative Research: Foundations of Adaptive Data Analysis, BIGDATA: F: DKA: Scalable, Private Algorithms for Continual Data Analysis, G6257167855, TWC: TTP Option: Frontier: Collaborative: MACS: A Modular Approach to Cloud Security, G6326212086, G7685818843, AF: SMALL: Frontiers in Algorithmic Game Theory, G8876996369
OpenAlex ID: W2894938360