Tackling Racial Bias in AI Systems: Applying the Bioethical Principle of Justice and Insights from Joy Buolamwini’s “Coded Bias” and the “Algorithmic Justice League”
DOI:
https://doi.org/10.62865/bjbio.v16i1.129Keywords:
AI bias, racial bias, bioethics, justice, Algorithmic Justice League, Coded Bias, Joy Buolamwini, facial recognition, equitable algorithmsAbstract
This paper explores the issue of racial bias in artificial intelligence (AI) through the lens of the bioethical principle of justice, with a focus on Joy Buolamwini’s “Coded Bias” and the work of the “Algorithmic Justice League.” AI technologies, particularly facial recognition systems, have been shown to disproportionately misidentify individuals from marginalised racial groups, raising profound ethical concerns about fairness and equity. The bioethical principle of justice stresses the importance of equal treatment and the protection of vulnerable populations. Through qualitative research, including content analysis of Buolamwini’s works and case studies of AI bias, this paper assesses the efforts of the Algorithmic Justice League to combat racial bias in AI. It emphasises their advocacy for the development of fair, equitable algorithms and calls for systemic reform in AI development to ensure justice for marginalised communities.
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