2020/07/28 by Susan Leavy, Barry O’Sullivan, Leavy, Susan +5 · 4 citations
Computer Science · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Computers and Society (cs.CY) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #cs.CY
paper · pdf · doi:10.48550/arxiv.2008.07341
3 pages
arxiv created 2020/07/28 · openalex publication_date 2020/07/28 · arxiv updated 2020/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty. Data used to train machine learning algorithms may capture social injustices, inequality or discriminatory attitudes that may be learned and perpetuated in society. Attempts to address this issue are rapidly emerging from different perspectives involving technical solutions, social justice and data governance measures. While each of these approaches are essential to the development of a comprehensive solution, often discourse associated with each seems disparate. This paper reviews ongoing work to ensure data justice, fairness and bias mitigation in AI systems from different domains exploring the interrelated dynamics of each and examining whether the inevitability of bias in AI training data may in fact be used for social good. We highlight the complexity associated with defining policies for dealing with bias. We also consider technical challenges in addressing issues of societal bias.