Abstract
In the field of explainable artificial intelligence (XAI), methods are being developed to explain AI results. These methods form the range of implementation choices available to XAI designers when dealing with the explainability requirements to a system. While in the discipline of Requirements Engineering, explainability has been conceptualized and operationalized as a nonfunctional requirement, there was so far little focus specifically on the quality aspects of the explanations themselves. Yet, quality requirements issues pertaining to the explanations of AI systems lead to issues such as lack of transparency, trust, and user confidence. The present PhD research makes a step towards closing this gap. The research aims to formulate a solution for determining the quality of explanations in AI systems, particularly in the healthcare domain. We believe that this research will benefit healthcare professionals in maintaining confidence and trust in AI-based healthcare systems.
Original language | English |
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Title of host publication | Joint Proceedings of REFSQ-2023 Workshops, Doctoral Symposium, Posters & Tools Track, and Journal Early Feedback Track |
Subtitle of host publication | Co-located with REFSQ 2023. Barcelona, Catalunya, Spain, April 17, 2023 |
Editors | Alessio Ferrari, Birgit Penzenstadler, Irit Hadar, Shola Oyedeji, Sallam Abualhaija, Renata Guizzardi |
Publisher | CEUR |
Number of pages | 7 |
Publication status | Published - 2023 |
Event | 29th International Working Conference on Requirement Engineering, REFSQ 2023 - Barcelona, Spain Duration: 17 Apr 2023 → 20 Apr 2023 Conference number: 29 |
Publication series
Name | CEUR workshop proceedings |
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Publisher | Rheinisch Westfälische Technische Hochschule |
Volume | 3378 |
ISSN (Print) | 1613-0073 |
Conference
Conference | 29th International Working Conference on Requirement Engineering, REFSQ 2023 |
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Abbreviated title | REFSQ 2023 |
Country/Territory | Spain |
City | Barcelona |
Period | 17/04/23 → 20/04/23 |
Keywords
- Artificial Intelligence in medicine
- Empirical research method`
- Explainable artificial intelligence
- Healthcare
- Quality requirements
- Requirements for explanations