Documenting use cases in the affective computing domain using Unified Modeling Language

TL;DR

This paper introduces a UML-based methodology for documenting AI use cases, focusing on affective computing, to improve understanding of context, scope, and risks for trustworthy AI development.

Contribution

It proposes a novel UML-based approach for AI use case documentation, integrating legal and ethical considerations specific to affective computing.

Findings

UML diagrams effectively represent AI use case details.

Structured tables complement diagrams for comprehensive documentation.

Application examples demonstrate the methodology's practicality.

Abstract

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.