This article by Richard Muirhead explores how bias, originating in the training data, which often reflects existing societal inequalities, is perpetuated and amplified by AI models. The article examines the far-reaching consequences of this bias, particularly in high-stakes decision-making areas like healthcare and criminal justice, emphasizing the potential for exacerbating social injustices. Finally, it proposes several mitigation strategies, including improving data diversity, employing bias detection tools, implementing algorithmic fairness techniques, and promoting transparency and inclusive design practices to create more equitable and just AI systems.
