Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (Preprint)

BACKGROUND The significant advancements in applying Artificial Intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems, particularly in areas like healthcare, employment, criminal justice, and credit scoring. Such systems can lead to unfair outcomes and perpetuate existing inequalities. This survey paper offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. OBJECTIVE We review sources of bias, such as data, algorithm, and human decision biases, and assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes. We explore various proposed mitigation strategies, discussing the ethical considerations of their implementation and emphasizing the need for interdisciplinary collaboration to ensure effectiveness. METHODS Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, and discuss the negative impacts of AI bias on individuals and society. We also provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. RESULTS Addressing bias in AI requires a holistic approach, involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. CONCLUSIONS This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias.