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Found 9 papers, 1 papers with code
Dissecting Causal Biases
Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individuals.
Shedding light on underrepresentation and Sampling Bias in machine learning
Sampling bias, is inconsistently used in the literature to describe bias due to the sampling procedure.
Survey on Fairness Notions and Related Tensions
Fairness requirements to be satisfied while learning models created several types of tensions among the different notions of fairness and other desirable properties such as privacy and classification accuracy.
On the Need and Applicability of Causality for Fair Machine Learning
Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense.
Causal Discovery for Fairness
Existing causal approaches to fairness in the literature do not address this problem and assume that the causal model is available.
Identifiability of Causal-based Fairness Notions: A State of the Art
This paper is a compilation of the major identifiability results which are of particular relevance for machine learning fairness.
Survey on Causal-based Machine Learning Fairness Notions
Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc.
Machine learning fairness notions: Bridging the gap with real-world applications
Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities.
