2020/01/31 by Suzanne Tolmeijer, Markus Kneer, Cristina Sarasua +2
Computer Science · Engineering · Social Sciences · #Adversarial Robustness in Machine Learning #Digital Transformation in Industry #Ethics and Social Impacts of AI #Field (mathematics) #Implementation #Object (grammar) #Selection (genetic algorithm) #Taxonomy (biology) #cs.AI
paper · pdf · doi:10.1145/3419633
published as ACM Comput. Surv. 53, 6, Article 132 (December 2020), 38 pages · published version, journal paper, ACM Computing Surveys, 38 pages, 7 tables, 4 figures
openalex created_date 2020/01/30 · openalex publication_date 2020/12/30 · arxiv created 2021/01/22 · arxiv updated 2021/01/25 · openalex updated_date 2026/08/05
Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested.