A FRAMEWORK FOR ETHICAL AI: ADDRESSING SYSTEMIC BIAS

Abstract

Artificial Intelligence (AI) is rapidly becoming an integral part of modern infrastructure, influencing critical decisions in sectors like healthcare, finance, and criminal justice. However, the proliferation of AI has highlighted the significant challenge of systemic bias, which can perpetuate and amplify discrimination. This review paper aims to synthesize existing knowledge to propose a comprehensive framework for ethical AI, with a specific focus on mitigating systemic bias. The methodology involves a systematic literature review of academic and gray literature published between 2015 and 2025, sourced from databases like IEEE Xplore, ACM Digital Library, and Google Scholar, alongside reports from technology firms and government bodies. The analysis reveals that bias in AI is a multi-faceted issue stemming from data, algorithms, and human factors. The results from case studies in criminal justice, employment, and healthcare demonstrate the profound real-world consequences of biased AI. In conclusion, this paper proposes a multi-dimensional framework that integrates technical interventions for bias mitigation, robust organizational governance, and proactive regulatory measures. This framework offers practical recommendations for developers, policymakers, and researchers to foster a more equitable and just AI ecosystem, asserting that addressing systemic bias is a socio-technical imperative.