Equalizing Financial Impact in Supervised Learning
Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairness criteria from Hardt, Price, and Srebro [NIPS, 2016] that makes a small step towards addressing this issue in the case of financial decisions like giving loans. We call this new notion "equalized financial impact."
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessGeneral ClassificationSimilar Papers 제목 키워드 기반
Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech
Mean Opinion Score (MOS) is a popular measure for evaluating synthesized speech. However, the scores obtained in MOS tests are heavily dependent upon many contextual factors. One such factor is the overall range of quali…
Learning-based density-equalizing map
Density-equalizing map (DEM) serves as a powerful technique for creating shape deformations with the area changes reflecting an underlying density function. In recent decades, DEM has found widespread applications in fie…
Data VisualizationModeling Financial Products and their Supply Chains
The objective of this paper is to explore how financial big data and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which w…
Predicting Financial Literacy via Semi-supervised Learning
Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data i…
regressionPredicting Financial Literacy via Semi-supervised Learning
Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data i…
regression