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.
Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
Artificial Intelligence in Governance, Risk and Compliance: Results of a study on potentials for the application of artificial intelligence (AI) in governance, risk and compliance (GRC)
Ethical Considerations in Artificial Intelligence Courses
University of Kentucky（肯塔基大学）；University of Southern California（南加州大学）；University of Michigan（密歇根大学）；IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）；UNSW（新南威尔士大学）；Data61/CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；TU Berlin（柏林工业大学）
Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；International Computer Science Institute（国际计算机科学研究所）；University of Minnesota（明尼苏达大学）；The Ohio State University（俄亥俄州立大学）；Syracuse University（雪城大学）；Human Computation Institute（人类计算研究所）
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
University of Oxford（牛津大学）；Cisco Research Centre（思科研究中心）；Cardiff University（卡迪夫大学）；University of Warwick（华威大学）
