Communicating Uncertainty in AI-Based Decision Support Systems: A Comparative Study of Numerical and Visual Representations

AI-assisted Decision-making and Consumers' Trust in AI Recommendations

Analysis of consumers' trust in AI-generated recommendations under conditions of AI-assisted decision-making shows that emotional trust may be a mediator in the intention to delegate decision-making to AI agents and proposes strategies to build more transparent and trustworthy AI recommendation systems that can improve the user experience.

Are Concept Bottleneck Models Effective as Decision-Support Systems?

Concept Bottleneck Models (CBMs) are interpretable-by-design neural networks that detect human-understandable concepts from the input and use them to generate predictions. By allowing users to inspect the concepts underlying a prediction and explore how predictions change under alternative concept configurations, CBMs have emerged as one of the most prominent approaches to supporting human-AI collaboration. However, user studies investigating their actual effectiveness as decision-support systems remain limited. We present two large-scale user studies (N participants = 705, N observations = 6,959) evaluating how concept-based explanations and user interventions on the model's concepts affect the performance of the human-AI team in two distinct binary classification tasks. Our results show that CBMs, and particularly their interactive component, can improve human-AI team accuracy relative to both unaided human performance and performance with non-interpretable AI support. However, these benefits emerge only under certain conditions: classification tasks perceived as difficult, easily identifiable concepts, and active interaction with the model. We also discuss how inaccurate concept detection may undermine users'trust in the model. Overall, this work provides practical guidance for the deployment of CBMs as effective decision-support tools.

One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread

Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative, and conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.

Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning

It is argued that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.

The Influence of AI Systems on Military Decision-Making

From Explanations to Reflective Questions in Human-AI Decision-Making

Many decision support systems (DSS) provide predictions, recommendations, and, increasingly, explanations. Supporting human-AI decision-making with context-specific questions, however, remains largely unexplored. Questions can stimulate reflection and critical thinking, thereby introducing productive friction in the decision-making process and potentially reducing overreliance on DSS. This paper presents a proof-of-concept for generating data-driven questions based on a DSS prediction and its corresponding explanation, i.e., feature contribution, using a local language model. We illustrate our method using a realistic example from the medical field. In informal discussions (n = 2), gathering views on the possible usefulness of questions in decision-making, the clinicians mentioned that the generated questions have potential to help them reconsider the prediction and consider alternative options. Our proof-of-concept informs the design of human-AI interactions aimed at promoting the cognitive engagement of decision-makers and mitigating overreliance on DSS by shifting the focus from explanations to questions.