Mitigating Bias in AI Models through Ethics and Transparency

Abstract

Artificial Intelligence (AI) systems have become ubiquitous, transforming various sectors such as healthcare, finance, and criminal justice. However, the potential for bias in these systems raises ethical and practical concerns. This paper delves into the sources of bias in AI, including data bias, algorithmic bias, and human bias. Real-world examples illustrate the impacts of biased AI, such as discriminatory lending practices, misdiagnoses in healthcare, and wrongful convictions in criminal justice. The ethical implications of biased AI are explored, emphasizing the need for fairness, equity, and transparency.Mitigation strategies are discussed, focusing on techniques like data cleansing, algorithmic auditing, and ethical AI design principles. Additionally, the importance of regulation and policy frameworks to address bias in AI is highlighted. By promoting ethical and transparent AI practices, this paper aims to contribute to the development of fairer and more responsible AI systems.