arXiv subjects

Pim Haselager

Publications and source records attributed to Pim Haselager.

6 recordsLinked to original sources

arXiv2026

Friction in AI-Assisted Clinical Decision-Making: A Case Study on The Role of Questions and 'What-if' Scenarios

Clinical decision-making is augmented by decision-support systems (DSSs). To counter overreliance on DSSs, several methods have been proposed that create friction in order to promote cognitive engagement and reflection. In this paper, we investigate how two such forms of friction, namely data-driven questions and `what-if' analysis, are perceived by medical experts. For a real-world decision task, we replicated a DSS used in clinical practice and gathered clinicians' feedback on a prototype through in-situ interviews (n=7). Our findings suggest that while the questions were perceived as unhelpful for reflective thinking, they could serve as reminders to consider relevant information. Furthermore, inspecting `what-if' hypotheticals was found useful for potentially improving patient care. Clinicians saw our prototype as a promising training tool for novice clinicians. From the clinicians' feedback, we make recommendations for designing friction in work practices. Our work contributes to human-AI interaction research, which aims to encourage reflection to mitigate AI overreliance.

cs.HC↗

arXiv2026

Supporting Reflection and Forward-Looking Reasoning With Data-Driven Questions

Many generative AI systems as well as decision-support systems (DSSs) provide operators with predictions or recommendations. Various studies show, however, that people can mistakenly adopt the erroneous results presented by those systems. Hence, it is crucial to promote critical thinking and reflection during interaction. One approach we are focusing on involves encouraging reflection during machine-assisted decision-making by presenting decision-makers with data-driven questions. In this short paper, we provide a brief overview of our work in that regard, namely: 1) the development of a question taxonomy, 2) the development of a prototype in the medical domain and the feedback received from clinicians, 3) a method for generating questions using a large language model, and 4) a proposed scale for measuring cognitive engagement in human-AI decision-making. In doing so, we contribute to the discussion about the design, development, and evaluation of tools for thought, i.e., AI systems that provoke critical thinking and enable novel ways of sense-making.

cs.HC↗

arXiv2025

A Taxonomy of Questions for Critical Reflection in Machine-Assisted Decision-Making

Decision-makers run the risk of relying too much on machine recommendations, which is associated with lower cognitive engagement. Reflection has been shown to increase cognitive engagement and improve critical thinking and therefore decision-making. Questions are a means to stimulate reflection, but there is a research gap regarding the systematic creation and use of relevant questions for machine-assisted decision-making. We therefore present a taxonomy of questions aimed at promoting reflection and cognitive engagement in order to stimulate a deliberate decision-making process. Our taxonomy builds on the Socratic questioning method and a question bank for explainable AI. As a starting point, we focus on clinical decision-making. Brief discussions with two medical and three educational researchers provide feedback on the relevance and expected benefits of our taxonomy. Our work contributes to research on mitigating overreliance in human-AI interactions and aims to support effective human oversight as required by the European AI Act.

cs.HC↗

arXiv2023

Minding rights: Mapping ethical and legal foundations of 'neurorights'

The rise of neurotechnologies, especially in combination with AI-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty - often framed as 'neurorights' in ethical, legal and policy discussions. Several states are now looking at including 'neurorights' into their constitutional legal frameworks and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of 'neurorights' the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or are in conflict with each other. The aim of this multidisciplinary work here is to provide conceptual, ethical and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity and cognitive liberty to facilitate scholarly, legal and policy discussions.

q-bio.NC↗

arXiv2020

Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations

In this paper we introduce the temporally factorized 3D convolution (3TConv) as an interpretable alternative to the regular 3D convolution (3DConv). In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter where the 2D slices are sequentially dependent on each other in the temporal dimension. We demonstrate that 3TConv learns temporal transformations that afford a direct interpretation. The temporal parameters can be used in combination with various existing 2D visualization methods. We also show that insight about what the model learns can be achieved by analyzing the transformation parameter statistics on a layer and model level. Finally, we implicitly demonstrate that, in popular ConvNets, the 2DConv can be replaced with a 3TConv and that the weights can be transferred to yield pretrained 3TConvs. pretrained 3TConvnets leverage more than a decade of work on traditional 2DConvNets by being able to make use of features that have been proven to deliver excellent results on image classification benchmarks.

cs.CV↗

arXiv2018

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods / interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.

cs.AI↗