From Bias to Fairness: A Review of Ethical Considerations and Mitigation Strategies in Artificial Intelligence

Abstract

Abstract: Artificial intelligence (AI) has become increasingly popular in recent years and has been used in a range of industries to improve outcomes, streamline processes, and improve decision-making. But there are also moral questions raised by the employment of AI, particularly in light of potential bias and discrimination. In order to promote justice and reduce bias, this paper offers a thorough discussion of ethical issues and mitigation techniques in AI. The evolution of AI and its possible advantages and disadvantages are first covered in the paper. After that, it explores the different ethical issues surrounding AI, such as trust, accountability, fairness, and openness. The study emphasises the effects of bias and discrimination on AI systems as well as the possible outcomes of these problems. The study also discusses the various mitigation measures, such as algorithmic strategies, data pre-processing, and model validation, that have been suggested to mitigate bias and enhance justice in AI. In order to develop the subject of AI ethics, the study analyses the advantages and disadvantages of different frameworks and emphasises the necessity of continued interdisciplinary research and collaboration. The study's importance in advancing ethical concerns and fairness in AI is highlighted in the paper's conclusion. It offers information about the state of the field at the moment and points out potential directions for further study. Overall, the article is a useful tool for academics, professionals, and decision-makers who want to support ethical and responsible AI development and application.