Fairness and Decision-making in Collaborative Shift Scheduling Systems

AI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityImpact of Automation on WorkPhysicians, Nurses & CliniciansPsychiatrists & Psychotherapists

The strains associated with shift work decrease healthcare workers' well-being. However, shift schedules adapted to their individual needs can partially mitigate these problems. From a computing perspective, shift scheduling was so far mainly treated as an optimization problem with little attention given to the preferences, thoughts, and feelings of the healthcare workers involved. In the present study, we explore fairness as a central, human-oriented attribute of shift schedules as well as the scheduling process. Three in-depth qualitative interviews and a validating vignette study revealed that while on an abstract level healthcare workers agree on equality as the guiding norm for a fair schedule, specific scheduling conflicts should foremost be resolved by negotiating the importance of individual needs. We discuss elements of organizational fairness, including transparency and team spirit. Finally, we present a sketch for fair scheduling systems, summarizing key findings for designers in a readily usable way.

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https://hci.top/en/papers/chi/31785/2020

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DOI: https://doi.org/10.1145/3313831.3376656
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Source
CHI
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Year
2020
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Authors
4 authors
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Subtopics
AI-Assisted Decision-Making & Automation, AI Ethics, Fairness & Accountability, Impact of Automation on Work
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Physicians, Nurses & Clinicians, Psychiatrists & Psychotherapists
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Abstract only
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