ArticleJournal of the American Medical Informatics Association : JAMIA2021
Trading off accuracy and explainability in AI decision-making: findings from 2 citizens' juries.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.Journal of healthcare engineering · 2023Pooled it
- Toward trustworthy healthcare AI: designing academic research for translation readiness.Molecular systems biology · 2026Review
- Video-Algorithmic Patient Monitoring in Mental Health Inpatient Settings: Qualitative Study of Patient or Consumer, Clinician, and Vendor Perspectives.JMIR human factors · 2026Article
- Ethical Aspects of Artificial Intelligence in Precision Medicine for Obesity Management.JMA journal · 2026Review
- Solidarity or Segregation? ChatGPT Health and US Health Care Disparities.Journal of medical Internet research · 2026Article
- Good things come in threes: evaluating clinical utility of machine learning-derived clusters.JAMIA open · 2026Review
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- Proposing core competencies for physicians in using artificial intelligence tools in clinical practice.Internal medicine journal · 2025Article
- AI ethics for the everyday intensivist.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.BMC medical informatics and decision making · 2025Review
- Trade-off hacking: Have the cake and eat it too? Turning competing interests into win-wins in data-driven food technology.Open research Europe · 2025Article
- Explainable and reproducible AI: culturally responsive AI for health equity in minoritized groups.Frontiers in digital health · 2025Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Review
- Should AI models be explainable to clinicians?Critical care (London, England) · 2024Article
- Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.Journal of imaging informatics in medicine · 2024Article
- Achieving large-scale clinician adoption of AI-enabled decision support.BMJ health & care informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios. MATERIALS AND
methodsCitizens' juries are a form of deliberative democracy eliciting informed judgment from a representative sample of the general public around policy questions. We organized two 5-day citizens' juries in the UK with 18 jurors each. Jurors considered 3 AI systems with different levels of accuracy and explainability in 2 healthcare and 2 non-healthcare scenarios. Per scenario, jurors voted for their preferred system; votes were analyzed descriptively. Qualitative data on considerations behind their preferences included transcribed audio-recordings of plenary sessions, observational field notes, outputs from small group work and free-text comments accompanying jurors' votes; qualitative data were analyzed thematically by scenario, per and across AI systems.
resultsIn healthcare scenarios, jurors favored accuracy over explainability, whereas in non-healthcare contexts they either valued explainability equally to, or more than, accuracy. Jurors' considerations in favor of accuracy regarded the impact of decisions on individuals and society, and the potential to increase efficiency of services. Reasons for emphasizing explainability included increased opportunities for individuals and society to learn and improve future prospects and enhanced ability for humans to identify and resolve system biases.
conclusionCitizens may value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy. The public should therefore be actively consulted when developing policy on AI explainability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.