Multimodal AI systems promise more natural, expressive, and creatively rich interactions, yet they also complicate how users convey intent and maintain control. As these systems rapidly enter everyday practice, agency becomes shared between humans and machines, raising urgent questions about: fairness, trustworthiness, responsibility, and accountability; calibrated trust and appropriate reliance; creative authorship and co-creation; meaningful human–AI decision-making. The goal of this workshop is to clarify the concept of distributed agency and explore its implications for human and artificial creativity. Through presentations and hands-on activities, participants will collectively develop human-centered design pathways for multimodal AI interfaces.

Artificial Intelligence (AI) has become a pivotal tool in augmenting human decision-making across various domains, yet its influence on user decisions often lacks comprehensive evaluation. While technical performance metrics such as accuracy and efficiency dominate AI design, integrating human-centered approaches that consider trust and reliance remains underexplored. This study addresses the knowledge gap in understanding how AI systems influence decision-making quality, calibrated to user profiles, including their expertise, skills, professional role, confidence, and reliance tendencies. We present a novel and comprehensive metric framework for evaluating AI influence, emphasizing behavioral patterns and measurable improvements in decision outcomes beyond simple alignment with AI recommendations. The framework is applied to four medical domain case studies—MRI, ECG, X-ray, and ENDO – with user groups spanning specialists, sub-specialists, and trainees. Results reveal that while human and AI systems achieve high agreement rates (up to 81%), AI influence on decision quality varies significantly. Notably, X-ray decision-making showed the highest influence index (0.27), while MRI decisions exhibited substantial self-anchoring bias (6.94), undermining the potential positive impact of AI. Influence metrics unveiled nuances missed by agreement scores, highlighting domain-specific biases and opportunities to optimize AI-human interaction. This research underscores the necessity for adapting the type of AI system and affordance to user characteristics and attitudes of reliance to foster calibrated trust and improve decision outcomes. Our findings inform the design of AI systems that better support diverse user needs and align with human decisions, driving progress toward human-centered AI integration in high-stakes domains. • Developed novel metrics to evaluate AI’s influence beyond user agreement with AI. • Identified biases impacting AI influence, such as self-anchoring and automation bias. • Applied framework to four medical studies with 330 clinicians and 15,000 decisions. • Revealed up to 81% alignment but variances in appropriate reliance and influence. • Highlighted need for adaptive AI systems to match user expertise. • Demonstrated that influence metrics uncover dynamics missed by traditional reliance.

Abstract Whilst it is commonly reported that healthcare is set to benefit from advances in Artificial Intelligence (AI), there is a consensus that, for clinical AI, a gulf exists between conception and implementation. Here we advocate the increased use of situated design and evaluation to close this gap, showing that in the literature there are comparatively few prospective situated studies. Focusing on the combined human-machine decision-making process - modelling, exchanging and resolving - we highlight the need for advances in exchanging and resolving. We present a novel relational space - contextual dimensions of combination - a means by which researchers, developers and clinicians can begin to frame the issues that must be addressed in order to close the chasm. We introduce a space of eight initial dimensions, namely participating agents, control relations, task overlap, temporal patterning, informational proximity, informational overlap, input influence and output representation coverage. We propose that our awareness of where we are in this space of combination will drive the development of interactions and the designs of AI models themselves. Designs that take account of how user-centered they will need to be for their performance to be translated into societal and individual benefit.

This workshop explores the influence of AI systems on human decision-making - algorithmic authority - and the broader concept of technology dominance, which includes both positive and negative impacts of AI reliance. Drawing from diverse fields such as Human-AI Interaction, Sociology, Epistemology, and Cognitive Science, the workshop will discuss theoretical foundations, empirical studies, and design implications of AI’s role in shaping human judgment and behavior. The objectives are to examine in-depth the concepts of algorithmic authority and technology dominance, and identify metrics for their assessment. The workshop aims to foster interdisciplinary collaboration and produce practical design principles that help to counter risks associated to AI technology dominance and thus foster a responsible use of AI systems.

This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations, that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation following specific advice. Specifically, our support mechanism utilizes explanations by examples, featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called frictional AI, a general class of decision support aimed at achieving a useful compromise between the increase of decision effectiveness and the mitigation of cognitive risks, such as over-reliance, automation bias and deskilling. To illustrate an instance of frictional AI, we conducted an empirical user study to investigate its impact on the task of radiological detection of vertebral fractures in x-rays. Our study engaged 16 orthopedists in a 'human-first, second-opinion' interaction protocol. In this protocol, clinicians first made initial assessments of the x-rays without AI assistance and then provided their final diagnosis after considering the pro-hoc explanations. Our findings indicate that physicians, particularly those with less experience, perceived pro-hoc XAI support as significantly beneficial, even though it did not notably enhance their diagnostic accuracy. However, their increased confidence in final diagnoses suggests a positive overall impact. Given the promisingly high effect size observed, our results advocate for further research into pro-hoc explanations specifically, and into the broader concept of frictional AI.

Abstract In this article we provide an analysis focusing on clinical use of two deep learning-based automatic detection tools in the field of radiology. The value of these technologies conceived to assist the physicians in the reading of imaging data (like X-rays) is generally assessed by the human-machine performance comparison, which does not take into account the complexity of the interpretation process of radiologists in its social, tacit and emotional dimensions. In this radiological vision work, data which informs the physician about the context surrounding a visible anomaly are essential to the definition of its pathological nature. Likewise, experiential data resulting from the contextual tacit knowledge that regulates professional conduct allows for the assessment of an anomaly according to the radiologist’s, and patient’s, experience. These data, which remain excluded from artificial intelligence processing, question the gap between the norms incorporated by the machine and those leveraged in the daily work of radiologists. The possibility that automated detection may modify the incorporation or the exercise of tacit knowledge raises questions about the impact of AI technologies on medical work. This article aims to highlight how the standards that emerge from the observation practices of radiologists challenge the automation of their vision work, but also under what conditions AI technologies are considered “objective” and trustworthy by professionals.

In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.