Ethics, bias, and fairness challenges in artificial intelligence and machine learning

Abstract

The fast development of artificial intelligence and machine learning technologies in the area of crucial social activities has raised the level of concern about ethical interests, algorithmic discrimination, and justice in automated decision-making procedures. This literature review demonstrates that the ethical issues of AI development, bias reduction, and equity systems are complex phenomena, and the development of AI should be performed responsibly. The problem statement focuses on the increasing gap between technological improvement and moral responsibility, where the AI systems reproduce the discriminating trends, violate the rights to privacy, and deliver obscure judgments to influence basic human rights. Based on the PRISMA approach to conducting a systematic literature review, the research is a synthesis of the existing knowledge about the sources of bias in machine learning pipelines, metrics of fairness and trade-offs, ethical frameworks that govern AI, and new regulatory environments. The findings indicate that there remain ongoing problems with the attainment of algorithmic fairness across intersectional groups, between fairness definitions, in terms of black box transparency, and accountability mechanisms of AI-related harm. The crucial shortcomings that have been found are the low standardization of fairness measures, lack of equal representation in training data sets, and the lack of interdisciplinary interaction and emerging regulatory frameworks that are not keeping pace with technological advancement. The proposed review article will add to the overall perspective of any challenges that are present nowadays, practical mitigation measures and future research advancements that would be useful in creating reliable AI systems that meet human values and societal expectations.