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Application of ethical AI requirements to an AI solution use-case in healthcare domain

Author

Listed:
  • Zohreh Pourzolfaghar
  • Marco Alfano
  • Markus Helfert

Abstract

Purpose - This paper aims to describe the results of applying ethical AI requirements to a healthcare use case. The purpose of this study is to investigate the effectiveness of using open educational resources for Trustworthy AI to provide recommendations to an AI solution within the healthcare domain. Design/methodology/approach - This study utilizes the Hackathon method as its research methodology. Hackathons are short events where participants share a common goal. The purpose of this to determine the efficacy of the educational resources provided to the students. To achieve this objective, eight teams of students and faculty members participated in the Hackathon. The teams made suggestions for healthcare use case based on the knowledge acquired from educational resources. A research team based at the university hosting the Hackathon devised the use case. The healthcare research team participated in the Hackathon by presenting the use case and subsequently analysing and evaluating the utility of the outcomes. Findings - The Hackathon produced a framework of proposed recommendations for the introduced healthcare use case, in accordance with the EU's requirements for Trustworthy AI. Research limitations/implications - The educational resources have been applied to one use-case. Originality/value - This is the first time that open educational resources for Trustworthy AI have been utilized in higher education, making this a novel study. The university hosting the Hackathon has been the coordinator for the Trustworthy AI Hackathon (as partner to Trustworthy AI project).

Suggested Citation

  • Zohreh Pourzolfaghar & Marco Alfano & Markus Helfert, 2023. "Application of ethical AI requirements to an AI solution use-case in healthcare domain," American Journal of Business, Emerald Group Publishing Limited, vol. 38(3), pages 112-128, June.
  • Handle: RePEc:eme:ajbpps:ajb-12-2022-0201
    DOI: 10.1108/AJB-12-2022-0201
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