Ethical AI Systems for Bias-Resistant Decision Making in Business and Healthcare

Abstract

This paper gives an overview of how ethical AI systems can be integrated within business and healthcare decision-making processes, with an emphasis on bias detection, transparency, and the imposition of fairness limitations. The study explores models that allow AI systems to detect bias in themselves, justify decision-making processes, and enforce formal fairness conditions when making high-stakes transactions like lending, pricing, approving contracts, and supporting clinical decisions. The paper discusses successful and unsuccessful AI implementations in the case studies of finance, healthcare, and recruitment to identify the effects of biased decision-making. Among the key findings, it is indicated that the use of AI systems can improve the accuracy and efficiency of decision-making, but the issue of bias has also become a major concern. This paper highlights the importance of open, self-correcting artificial intelligence that will be just and accountable. The potential implications of the research are that the study might allow improving business and clinical practices, increasing consumer trust, and creating common frameworks of ethical AI implementation in industries.