Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements

Fuente: arXiv
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Autores principales: Umm-e-Habiba, Bogner, Justus, Wagner, Stefan
Formato: Preprint
Publicado: 2022
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author Umm-e-Habiba
Bogner, Justus
Wagner, Stefan
author_facet Umm-e-Habiba
Bogner, Justus
Wagner, Stefan
contents With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems. Explanations help to reduce system opacity, support transparency, and increase stakeholder trust. In this position paper, we discuss synergies between requirements engineering (RE) and Explainable AI (XAI). We highlight challenges in the field of XAI, and propose a framework and research directions on how RE practices can help to mitigate these challenges.
format Preprint
id arxiv_https___arxiv_org_abs_2206_01507
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
Umm-e-Habiba
Bogner, Justus
Wagner, Stefan
Software Engineering
Artificial Intelligence
Machine Learning
With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems. Explanations help to reduce system opacity, support transparency, and increase stakeholder trust. In this position paper, we discuss synergies between requirements engineering (RE) and Explainable AI (XAI). We highlight challenges in the field of XAI, and propose a framework and research directions on how RE practices can help to mitigate these challenges.
title Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
topic Software Engineering
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2206.01507