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What is Predictive Parity

Encyclopedia of Data Science and Machine Learning
A fairness metric that checks whether, for a given classifier, the precision rates are equivalent for subgroups under consideration.
Published in Chapter:
Fairness Challenges in Artificial Intelligence
Shuvro Chakrobartty (Dakota State University, USA) and Omar F. El-Gayar (Dakota State University, USA)
Copyright: © 2023 |Pages: 18
DOI: 10.4018/978-1-7998-9220-5.ch101
Fairness is a highly desirable human value in day-to-day decisions that affect human life. In recent years many successful applications of AI systems have been developed, and increasingly, AI methods are becoming part of many new applications for decision-making tasks that were previously carried out by human beings. Questions have been raised: 1) Can the decision be trusted? 2) Is it fair? Overall, are the AI-based systems making fair decisions, or are they increasing the unfairness in society? This article presents a systematic literature review (SLR) of existing works on AI fairness challenges. Towards this end, a conceptual bias mitigation framework for organizing and discussing AI fairness-related research is developed and presented. The systematic review provides a mapping of the AI fairness challenges to components of a proposed framework based on the suggested solutions within the literature. Future research opportunities are also identified.
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