Fairness and Decision-making in Collaborative Shift Scheduling Systems
Authors
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.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 80%
How Much Decision Power Should (A)I Have?: Investigating Patients’ Preferences Towards AI Autonomy in Healthcare Decision Making
CHI '24· AI-Assisted Decision-Making & Automation +1
- 67%
Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis: Assertiveness-based BreastScreening-AI
CHI '23· Explainable AI (XAI) +2
- 67%
Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment
CHI '24· AI-Assisted Decision-Making & Automation +2
- 67%
Toward Patient-Centered AI Fact Labels: Leveraging Extrinsic Trust Cues
DIS '25· Explainable AI (XAI) +2
Based on Jaccard similarity of research subtopics & professions (≥60%)