The Socio-Legal Relevance of Artificial Intelligence

Abstract:
This article draws on socio-legal theory in relation to growing concerns over fairness, accountability and transparency of societally applied artificial intelligence (AI) and machine learning. The purpose is to contribute to a broad socio-legal orientation by describing legal and normative challenges posed by applied AI. To do so, the article first analyzes a set of problematic cases, e.g., image recognition based on gender-biased databases. It then presents seven aspects of transparency that may complement notions of explainable AI (XAI) within AI-research undertaken by computer scientists. The article finally discusses the normative mirroring effect of using human values and societal structures as training data for learning technologies; it concludes by arguing for the need for a multidisciplinary approach in AI research, development, and governance.

This article draws on socio-legal theory in relation to growing concerns over fairness, accountability and transparency of societally applied artificial intelligence (AI) and machine learning. The purpose is to contribute to a broad socio-legal orientation by describing legal and normative challenges posed by applied AI. To do so, the article first analyses a set of problematic cases, e.g. image recognition based on gender-biased databases. It then presents seven aspects of transparency that may complement notions of explainable AI within computer scientific AI-research. The article finally discusses the normative mirroring effect of using human values and societal structures as training data for learning technologies, and concludes by arguing for the need for a multidisciplinary approach in AI research, development and governance.