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

arXiv: https://arxiv.org/abs/2209.09666 · HTML: https://mlsanity.com/paper/2209.09666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey](https://mlsanity.com/paper/1911.05609/) (2019)
- [AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations](https://mlsanity.com/paper/2101.12284/) (2021)
- [An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence](https://mlsanity.com/paper/2107.13734/) (2021)

## Built on by (most cited first, 1 of 1)

- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)

## Abstract (arXiv)

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.