Sovrano, Francesco Miller, Tim 2025 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. application/pdf https://n2t.net/ark:/12658/srd1337110 https://susi.usi.ch/usi/documents/337110 https://susi.usi.ch/documents/337110/files/Sovrano_2025_Springer_MAGIX_Accepted_Manuscript.pdf eng info:eu-repo/semantics/altIdentifier/isbn/9783032019394 info:eu-repo/semantics/altIdentifier/isbn/9783032019400 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-032-01940-0_6 info:eu-repo/semantics/altIdentifier/ark/12658/srd1337110 info:eu-repo/semantics/openAccess License undefined Enterprise AI / Shazia Sadiq. - Springer Nature Switzerland. - 2025, p. 183-209 Explainable AI Enterprise AI Reliance Machine-in-the-loop info:eu-repo/classification/udc/004 MAGIX : a unified framework for the use of XAI in enterprises http://purl.org/coar/resource_type/c_3248