The Effect of AI Advice on Human Confidence in Decision-Making

As artificial intelligence advances, it can increasingly be applied in collaborative decision-making contexts with humans. However, questions on the design of different collaborative environments remain open. In the context of AI-assisted human decision-making processes, we analyze the influence of AI advice on human confidence in the final decision. In a laboratory experiment, 458 subjects performed an image classification task. We compare their confidence over three treatments: i) a baseline case where subjects do not receive any AI advice; ii) where subjects receive AI advice; and iii) in addition to AI advice subjects also see the certainty of AI for its choice. Our results suggest that while AI advice can increase human overconfidence, this effect can be mitigated by augmenting the AI advice with its certainty. Our result not only contributes to the growing literature of human-AI collaboration, but also bears important practical implications for the design of collaborative systems

Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation

We study how humans make decisions when they collaborate with an artificial intelligence (AI): each instance of a classification task could be classified by themselves or by the AI. Experimental results suggest that humans and AI who work together can outperform the superior AI when it works alone. However, this only occurred when the AI delegated work to humans, not when humans delegated work to the AI. The AI profited, even from working with low-performing subjects, but humans did not delegate well. This bad delegation performance cannot be explained with algorithm aversion. On the contrary, subjects tried to follow a provided delegation strategy diligently and appeared to appreciate the AI support. However, human results suffered due to a lack of metaknowledge. They were not able to assess their own capabilities correctly, which in turn leads to poor delegation decisions. In contrast to reluctance to use AI, lacking metaknowledge is an unconscious tr

Planning for overtime: the value of shift extensions in physician scheduling

Scheduling physicians is a key success factor in hospitals. Heterogeneous demand and 24/7 service make the problem challenging. Approaches in the literature use flexible shift patterns to match demand with scarce resources. In these approaches, demand is usually assumed to be deterministic. However, surgery durations and emergency arrivals are both uncertain, leading to massive staff overtime. We introduce stochastic demand for physicians using a scenario-based approach. To incorporate this in scheduling, we allow variable shift extensions. If a variable shift extension is scheduled, the physician knows that with a given probability he or she may have to work a few periods longer. Thus, we ensure a matching of supply with demand, and at the same time we increase predictability of working hours. We propose a mixed-integer model and a column generation heuristic to solve our problem and provide experimental data from a German university hospital. Our approach reduces unplanned overtime by more than 80%, given a constant workforce. In cases of similar levels of unplanned overtime, the required workforce level can be decreased by 20%. Our approach aims at improving physicians' work-life balance and provides insights for hospitals' contract design processes

The potential of patient‑based nurse staffing – a queuing theory application in the neonatal intensive care setting

Faced by a severe shortage of nurses and increasing demand for care, hospitals need to optimally determine their staffing levels. Ideally, nurses should be staffed to those shifts where they generate the highest positive value for the quality of healthcare. This paper develops an approach that identifies the incremental benefit of staffing an additional nurse depending on the patient mix. Based on the reasoning that timely fulfillment of care demand is essential for the healthcare process and its quality in the critical care setting, we propose to measure the incremental benefit of staffing an additional nurse through reductions in time until care arrives (TUCA). We determine TUCA by relying on queuing theory and parametrize the model with real data collected through an observational study. The study indicates that using the TUCA concept and applying queuing theory at the care event level has the potential to improve quality of care for a given nurse capacity by efficiently trading situations of high versus low workload