عنوان

Achieving Causal Fairness in Machine Learning

Number

TLpq2404081566

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Achieving Causal Fairness in Machine Learning

General Material Designation

[Thesis]

First Statement of Responsibility

Wu, Yongkai

Subsequent Statement of Responsibility

Wu, Xintao

Name of Publisher, Distributor, etc.

University of Arkansas

Date of Publication, Distribution, etc.

2020

Specific Material Designation and Extent of Item

162

Dissertation or thesis details and type of degree

Ph.D.

Body granting the degree

University of Arkansas

Text preceding or following the note

2020

Text of Note

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

Electronic name

p

[Thesis]

276903

a

Y