mlsanityData to 14 Oct 2026

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions

arXiv 2602.00259 ↗First posted 30 Jan 2026cs.HC cs.AI q-bio.OTcited by 0

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes underneath. We address this gap by reconsidering AI interfaces as collections of intelligent reasoning cues: discrete pieces of AI information that can individually influence decision-making. We then explore the roles of eight types of reasoning cues in a high-stakes clinical decision (treating patients with sepsis in intensive care). Through contextual inquiries with six teams and a think-aloud study with 25 physicians, we find that reasoning cues have distinct patterns of influence that can directly inform design. Our results also suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 95 references that are in the map. First posted on arXiv 30 Jan 2026.

Builds on

26 of 95 references.

References · 26

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  3. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  4. 2024When combinations of humans and AI are useful: A systematic review and meta-analysiscited by 106
  5. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 86
  6. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 83
  7. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  8. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  9. 2019"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learningcited by 39
  10. 2022Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Supportcited by 39
  11. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
  12. 2023Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Carecited by 37
14 more
  1. 2023Improving Human-AI Collaboration With Descriptions of AI Behaviorcited by 36
  2. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  3. 2023Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosiscited by 30
  4. 2021Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Makingcited by 19
  5. 2023Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic reviewcited by 18
  6. 2021Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatmentcited by 16
  7. 2025Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gapcited by 14
  8. 2023Overcoming Algorithm Aversion: A Comparison between Process and Outcome Controlcited by 12
  9. 2025AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Supportcited by 12
  10. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  11. 2019Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?cited by 9
  12. 2024Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unitcited by 6
  13. 2025Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Taskscited by 6
  14. 2024How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Modelscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
  2. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making
  3. 2024Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
  4. 2026Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators
  5. 2025A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems
  6. 2024Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios
  7. 2025AI reasoning effort predicts human decision time in content moderation
  8. 2025Exploring Efficiency Frontiers of Thinking Budget in Medical Reasoning: Scaling Laws between Computational Resources and Reasoning Quality