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A quality of mercy is not trained: the imagined vs. the practiced in healthcare process-specialized AI development

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  • Anand Bhardwaj
  • Samer Faraj

Abstract

In high stakes organizational contexts like healthcare, artificial intelligence (AI) systems are increasingly being designed to augment complex coordination tasks. This paper investigates how the ethical stakes of such systems are shaped by their epistemic framings: what aspects of work they represent, and what they exclude. Drawing on an embedded study of AI development for operating room (OR) scheduling at a Canadian hospital, we compare scheduling-as-imagined in the AI design process: rule-bound, predictable, and surgeon-centric, with scheduling-as-practiced as a fluid, patient-facing coordination process involving ethical discretion. We show how early representational decisions narrowed what the AI could support, resulting in epistemic foreclosure: the premature exclusion of key ethical dimensions from system design. Our findings surface the moral consequences of abstraction and call for a more situated approach to designing healthcare process-specialized artificial intelligence systems.

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  • Anand Bhardwaj & Samer Faraj, 2025. "A quality of mercy is not trained: the imagined vs. the practiced in healthcare process-specialized AI development," Papers 2510.21843, arXiv.org.
    • Handle: RePEc:arx:papers:2510.21843
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    1. Stella Pachidi & Hans Berends & Samer Faraj & Marleen Huysman, 2021. "Make Way for the Algorithms: Symbolic Actions and Change in a Regime of Knowing," Organization Science, INFORMS, vol. 32(1), pages 18-41, January.
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