MAGIX : a unified framework for the use of XAI in enterprises
- Sovrano, Francesco ORCID Istituto di sistemi informatici (SYS), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera - Collegium Helveticum, ETH Zurich, Switzerland - Department of Informatics, University of Zurich, Switzerland
- Miller, Tim School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
- 2025
Published in:
- Enterprise AI / Shazia Sadiq. - Springer Nature Switzerland. - 2025, p. 183-209
English
This chapter proposes a framework for explainable artificial intelligence (XAI) in enterprises. XAI has long been touted as a way to mitigate several issues in enterprise AI systems: (dis)trust, over- and under-reliance, opaqueness, lack of understanding, lack of compliance, bias and discrimination, and poor human decision-making. We argue that, despite having made progress in some of these areas, such as improving understanding and compliance, progress in others has been stunted due to incorrect assumptions about how AI tools are used and understood. We map out a framework for explainable AI that considers some recent positions on the use of explainable AI, with a focus on decision-making in enterprises.
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- Language
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- English
- Classification
- Computer science and technology
- License
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License undefined
- Open access status
- green
- Identifiers
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- ISBN 9783032019394
- ISBN 9783032019400
- DOI 10.1007/978-3-032-01940-0_6
- RICERCO 55186
- ARK ark:/12658/srd1337110
- Persistent URL
- https://n2t.net/ark:/12658/srd1337110
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