Objective metrics for ethical AI: a systematic literature review

Abstract The field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.