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Fairness in machine learning: definition, testing, debugging, and application
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DOI：10.1007/s11432-023-4060-x.png)
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摘要
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In recent years, artificial intelligence technology has been widely used in many fields, such as computer vision, natural language processing and autonomous driving. Machine learning algorithms, as the core technique of AI, have significantly facilitated people's lives. However, underlying fairness issues in machine learning systems can pose risks to individual fairness and social security. Studying fairness definitions, sources of problems, and testing and debugging methods of fairness can help ensure the fairness of machine learning systems and promote the wide application of artificial intelligence technology in various fields. This paper introduces relevant definitions of machine learning fairness and analyzes the sources of fairness problems. Besides, it provides guidance on fairness testing and debugging methods and summarizes popular datasets. This paper also discusses the technical advancements in machine learning fairness and highlights future challenges in this area.
Keyword:
artificial intelligence security
machine learning security
machine learning fairness
model testing
model debugging
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7.6
论文数:
4.9K
被引数:
8.9K
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