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  4. Vol 7, Issue 3, 2024

Systemic Bias in Artificial Intelligence: Focusing on Gender, Racial, and Political Biases

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DOI: 10.23977/jaip.2024.070324 | Downloads: 23 | Views: 455

Author(s)

Yefan Zhu 1

Affiliation(s)

1 Department of Art and Technology, School of the Art Institute of Chicago, 1140 S Wabash Ave, Chicago, IL, 60605, America

Corresponding Author

Yefan Zhu

ABSTRACT

This paper examines systemic bias in artificial intelligence (AI), focusing on gender, racial, and political dimensions. As AI technology has evolved from theoretical frameworks to practical applications across social and cultural realms—ranging from collaborative robots to natural language processing—it has achieved significant advancements. However, this transition has highlighted a critical tension between AI's precise algorithms and the intricate dynamics of human society, revealing how systemic biases can diverge from ethical standards and perpetuate inequality. By delving into these biases, this study aims to illuminate the ways AI can unjustly advantage or disadvantage specific groups, ultimately contributing to a deeper understanding of the ethical implications of AI technologies in contemporary society.

KEYWORDS

Artificial Intelligence; Systemic Bias; Gender Bias; Racial Bias; Political Bias; Ethical Implications; Algorithmic Fairness; Social Dynamics; Technology and Society

CITE THIS PAPER

Yefan Zhu, Systemic Bias in Artificial Intelligence: Focusing on Gender, Racial, and Political Biases. Journal of Artificial Intelligence Practice (2024) Vol. 7: 195-206. DOI: http://dx.doi.org/10.23977/jaip.2024.070324.

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