Data Bias and Discrimination in AI: Addressing Social Justice Concerns through Legal Reform

Abstract

As artificial intelligence (AI) technologies become increasingly integral to decision-making across various sectors, the concerns surrounding data bias and discrimination have emerged as critical issues, particularly from a social justice perspective. This paper explores the intersection of data bias in AI systems and its implications for marginalized communities, highlighting how entrenched social inequities are often perpetuated or exacerbated by algorithmic decision-making. We examine case studies that illustrate the detrimental effects of biased data on outcomes in areas such as employment, law enforcement, and healthcare. The research underscores the necessity for comprehensive legal reform aimed at promoting fairness and accountability in AI deployment. We advocate for the implementation of regulatory frameworks that prioritize transparency in data collection, algorithmic processes, and the auditing of AI systems to identify and mitigate biases. Additionally, the paper argues for the establishment of ethical standards that incorporate principles of equity and justice, ensuring that AI technologies serve the public good rather than reinforce systemic discrimination. Ultimately, this study calls for a collaborative approach involving policymakers, technologists, and community stakeholders to foster a legal landscape that champions social justice in the age of AI.