عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TLpq2404081566
انگلیسی
Achieving Causal Fairness in Machine Learning
[Thesis]
Wu, Yongkai
Wu, Xintao
University of Arkansas
2020
162
Ph.D.
University of Arkansas
2020
Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders.
Algorithmic bias
Causal inference
Computer science
Fairness
Machine learning
Wu, Xintao
Wu, Yongkai
مطالعه متن کتاب p
[Thesis]
276903
a
Y
