ICML 2026 Position Papers
ICML 2026 Position Papers
We invite the submission of position papers to be published at the ICML 2026 conference. Position papers make an argument for a viewpoint or perspective about what
should
be done, in contrast to main track papers, which report on advances that have already been accomplished. Position papers also generally adopt a meta-level perspective on the field of machine learning, with wider scope than any individual area.
The review criteria for position papers differs from those of the main track. Submissions to the main ICML conference track emphasize original research and novel results. In contrast, submissions to the position paper track will be judged primarily on whether they present a compelling position that warrants greater exposure within the machine learning community (regardless of whether a reviewer agrees with the position). The goal of this track is to highlight papers that stimulate constructive, civil discussion on timely topics that need our community’s attention. Controversial topics are welcome.
Position papers should meet standard ICML expectations for scholarship, including the use of evidence and reasoning to support all claims, inclusion of relevant background and context, and the attribution of others’ work via appropriate citations. To constitute a proper scientific contribution, a position must be non-obvious, falsifiable and defendable against credible alternatives. We discourage technical papers that have a minor persuasive element, as well as nontechnical papers that promote unsupported opinion. Accepted position papers will be presented at the conference (as oral talks or posters) and included in the conference proceedings.
We want to hear your positions. What is the field getting right? Getting wrong? Position papers may address any aspect relevant to machine learning (ML), including but not limited to:
Concerns about data legality, copyright, and intellectual property in ML
The role of privacy in ML training and deployment
The role of open-source versus closed-source ML models for research
Regulation of ML technology (licensing, evaluation, disclosures, post-deployment monitoring, etc.)
Ethical considerations when conducting ML research and deploying ML systems
User guidance for responsible use of ML tools, services, applications, etc.
What the next generation of ML researchers needs to know
How we can improve the beneficial impact of our community’s work
We encourage you to browse the
position papers
that were published at ICML 2025 for examples (and ideas to build on, or from which to offer an alternative position).
Policies and Requirements
All requirements and policies are identical to those of the main conference (see the main track
Call for Papers
), with the following exceptions:
The Title should state the position and start with “Position:”.
The paper must include an “Alternative Views” section in the main body of the paper (not an appendix) that describes and addresses one or more credible (not strawmen) positions that are opposed to the paper’s position.
The following are not required for position papers:
Impact Statements (the position paper itself should explain the impact)
Lay Summaries (the position paper itself should be accessible to the public)
The reciprocal reviewing requirement
The self-ranking requirement.
Helpful tips on writing position papers:
Make sure the Title states the position.
These hypothetical paper titles do state a position:
"Position: Quantum Atelic Learning Methods Should Employ Psychic Insights"
"Position: Stop Research on Psychic Properties of Machine Learning"
while these versions do not:
"Position: Psychic Quantum Atelic Learning"
"Position: A Perspective on Psychic Quantum Atelic Learning"
The Abstract should identify the paper as a position paper and briefly state the position (e.g., “This position paper argues that
Executive Summary
The ICML 2026 Position Papers call invites submissions of viewpoint papers that argue for specific perspectives on machine learning. Position papers must meet ICML expectations for scholarship, be non-obvious, and falsifiable. The review criteria differ from the main track, focusing on the presentation of a compelling position rather than novel results. The conference aims to stimulate constructive discussion on timely topics, and submissions are encouraged on issues such as data legality, privacy, regulation, ethics, and user guidance. The paper highlights the importance of addressing these critical concerns in machine learning research and practice.
Key Points
- ▸ Position papers make an argument for a viewpoint or perspective on machine learning
- ▸ Review criteria differ from the main track, focusing on compelling positions rather than novel results
- ▸ Submissions are encouraged on issues such as data legality, privacy, regulation, ethics, and user guidance
Merits
Encouraging Constructive Discussion
The ICML 2026 Position Papers call aims to stimulate constructive, civil discussion on timely topics, which is essential for the advancement of machine learning research and practice.
Addressing Critical Concerns
The call highlights the importance of addressing critical concerns in machine learning research and practice, such as data legality, privacy, regulation, ethics, and user guidance.
Promoting Scholarship
Position papers must meet ICML expectations for scholarship, ensuring that submissions are well-reasoned and evidence-based.
Demerits
Limited Scope
The call may limit the scope of submissions to specific topics, which may not fully capture the breadth of machine learning research and practice.
Potential for Bias
The call's emphasis on compelling positions may lead to biased submissions, where authors may prioritize persuasive arguments over rigorous scholarship.
Unclear Review Criteria
The review criteria for position papers may not be clearly defined, which may lead to inconsistencies in the review process.
Expert Commentary
The ICML 2026 Position Papers call is a timely and important initiative that highlights the need for constructive discussion on critical concerns in machine learning research and practice. By encouraging submissions on topics such as data legality, privacy, regulation, ethics, and user guidance, the call aims to stimulate a more nuanced understanding of the complex issues surrounding machine learning. However, the call's limitations, such as its potential for bias and unclear review criteria, must be carefully considered to ensure that the review process is rigorous and fair. Ultimately, the success of the call will depend on the quality of submissions and the engagement of the machine learning community in addressing these critical concerns.
Recommendations
- ✓ The ICML 2026 Position Papers call should prioritize diversity and inclusivity in submissions to ensure that a wide range of perspectives are represented.
- ✓ The review criteria should be clearly defined and transparent to ensure consistency and fairness in the review process.
