Ukanwa Kalinda
University of California, Irvine, United States.
Curr Opin Psychol. 2024 Aug;58:101836. doi: 10.1016/j.copsyc.2024.101836. Epub 2024 Jul 1.
Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications.
算法偏见已成为人工智能（AI）责任生产时代的一项关键挑战。本文回顾了近期关于算法偏见的研究，并提议加强心理学和社会科学研究的参与，以了解算法偏见的成因和后果。通过三维可靠人工智能框架的视角，本文探讨了心理学等社会科学学科如何在人工智能生命周期的设计、开发和部署阶段，为识别和减轻偏见做出贡献。最后，我们提出了未来的研究方向，以进一步应对算法偏见的复杂性及其社会影响。
