2022/09/19 by Isabelle Hupont, Emília Gómez, Hupont, Isabelle +1
Computer Science · Psychology · Social Sciences · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Deception detection and forensic psychology #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2209.09666
openalex publication_date 2022/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The study of the ethical impact of AI and the design of trustworthy systems needs the analysis of the scenarios where AI systems are used, which is related to the software engineering concept of "use case" and the "intended purpose" legal term. However, there is no standard methodology for use case documentation covering the context of use, scope, functional requirements and risks of an AI system. In this work, we propose a novel documentation methodology for AI use cases, with a special focus on the affective computing domain. Our approach builds upon an assessment of use case information needs documented in the research literature and the recently proposed European regulatory framework for AI. From this assessment, we adopt and adapt the Unified Modeling Language (UML), which has been used in the last two decades mostly by software engineers. Each use case is then represented by an UML diagram and a structured table, and we provide a set of examples illustrating its application to several affective computing scenarios.