We invite you to submit your paper to the workshop on ‘Responsible Communication of Machine Learning Research in Biomedicine’, to be held as part NeurIPS 2026.
Our workshop treats structured, interdisciplinary dialogue as the method for closing the gap between what ML systems can deliver in biomedical contexts and how their capabilities are communicated to those who use or regulate them. We invite submissions that engage substantively with ML research practice, outputs or capabilities from an empirical, methodological, policy or science communication perspective.
We particularly encourage:
- Novel unpublished work and preliminary findings
- Emerging ideas as well as work-in-progress contributions
- Interdisciplinary approaches combining ML research with biomedical research, clinical use, policy or science communication
- Empirical studies that systematically evaluate communication approaches
- Case studies demonstrating real-world translation of ML research in biomedical context
Paper submission guidelines
OpenReview profile: All authors and reviewers must have an OpenReview profile by the submission deadline. New profiles created without an institutional email can take up to two weeks to be moderated and activated; profiles created with an institutional email are activated automatically (see the NeurIPS Main Track Handbook for more information).
Novelty: Submissions must be novel and not already published in an archival form elsewhere (including the NeurIPS 2026 main track, evaluations and datasets track, or position paper track).
Format: We offer two submission tracks — full papers (6–8 pages) for original research, empirical studies, technical contributions or case studies; and short papers or extended abstracts (2–3 pages) for work in progress or preliminary findings. Page limits exclude references and appendices.
Template: Use the standard NeurIPS 2026 LaTeX style files (available to download here).
Review protocol: Authors should anonymize their submissions to ensure a double blind review process.
LLM policy: In the preparation of your contributions, the use of LLMs is allowed only as a general-purpose writing assistance tool.
Supplementary material: No separate supplementary files are accepted. Additional analyses should be included as an appendix within the main PDF.
Code and data: Code or data accompanying a submission must be hosted on a public platform rather than shared as a zip file or drive link (exceptions require a clear scientific or ethical justification). All linked materials must be anonymised.
Submission: All submissions should be made via OpenReview.
Reviewer nomination: Every submission must nominate at least one author as a qualified reviewer, defined as at least one first-authored or three co-authored publications at a relevant peer-reviewed venue. Failure to nominate a qualified reviewer when one is available in the author list may result in desk rejection.
Publication: The workshop is non-archival. By default, accepted papers will be made publicly available on OpenReview.
Topics of interest
The tracks below are illustrative examples of relevant themes. We welcome submissions on any topic that engages substantively with using, evaluating, governing or communicating about machine learning (including foundation models and LLMs) in biomedicine.
Track 1: Empirical studies of ML capability claims in biomedicine
- Studies of audience comprehension, public understanding or misunderstanding of ML capabilities and limitations
- Empirical analysis of patterns in overstated or misrepresented capability claims
- Studies evaluating how capability claims influence trust or adoption decisions
- Studies of terminology and vocabulary gaps between ML and clinical, policy or science communication communities
- Comparisons across publication venues (e.g. papers, press releases, social media, clinician-facing materials)
Track 2: Methods and tools for communicating ML capabilities and limitations
- Evidence-based practices for translating ML concepts to non-experts
- Development of shared vocabulary or terminology frameworks to bridge ML and biomedical/clinical/policy communication
- Novel approaches to communicating model uncertainty and limitations, including for foundation models and LLMs
- Communication-focused evaluation metrics for ML systems
- Explainability and interpretability methods designed for non-technical stakeholders
Track 3: Methods for responsible ML adoption in biomedical research and healthcare
- Communication strategies that promote responsible adoption and use
- Community-centred approaches to AI communication in biomedical or clinical settings
- Methods for conveying uncertainty and model limitations to biomedical researchers or clinicians
- Studies evaluating communication during the implementation and deployment of ML systems in biomedical or clinical settings
- Barriers to responsible ML adoption in biomedical or clinical workflows
Track 4: Governance, reporting standards and policy communication
- Novel frameworks or standards for translating technical research into usage guidelines or policy recommendations
- Case studies from journalism, policy and public communication of AI
- Studies of how policymakers interpret technical ML findings and related impacts
- Evaluation of existing reporting standards (e.g. TRIPOD+AI, TRIPOD-LLM) and adherence to them
Track 5: Case studies of communication successes and failures across the biomedical ML pipeline
- Case studies of communication successes and failures in early biomedical discovery or clinical deployment
- Lessons learned from failed translation of ML systems into biomedical practice
- Communication across the research-to-clinic pipeline
- Cross-cultural studies of AI perception and understanding in biomedical and healthcare contexts
- How cultural and linguistic framing shape ML communication effectiveness
Presentation opportunities
Accepted papers will be presented in an interactive poster session on the day of the workshop. A subset of papers will be selected for presentation in short contributed talks with Q&As.
Important dates
Call for papers: Now open – submission via OpenReview
Submission deadline: 29 August 2026 (AOE)
Author notifications: 29 September 2026 (AOE)
Workshop date: 11 or 12 December 2026 (TBC)
Questions?
For questions about submissions, please contact the organizing committee at translating.ml.research@gmail.com
This workshop is part of NeurIPS 2026. For general conference information, visit neurips.cc.