Call for Papers

Call for Papers

We invite submissions to the ICLR 2025 Workshop on Human-AI Coevolution, and welcome paper submissions from all areas of machine learning.

Questions can be directed to: iclr25haic@gmail.com

Submit your work at the OpenReview Link here.

Key dates

The planned dates are as follows (all times are UTC-12h, aka “Anywhere on Earth”):

• Submission Deadline: 10 February, 2025 

• Notification of Acceptance: 3 March, 2025

• Camera-Ready Paper Due: 27 March, 2025

• Workshop Time: 27 April, 2025

Subject Areas

We invite contributions that address various aspects of human-AI coevolution (HAIC) from diverse disciplines. Submissions should align with the overarching goal of the workshop, which is to explore the intricate interaction between humans and AI systems over extended periods. 

We welcome submissions of either (i) work that provides innovative insights, case studies, empirical analyses, and theoretical contributions addressing HAIC, (ii) position papers that make relevant arguments about HAIC, or (ii) expressions of interest in which prospective attendees describe their general background and interests in HAIC.

In particular, we are interested in work that delve into the following subject areas:

1. Human-AI Interaction and Alignment

• Evolution of human expectations and trust in AI systems

• Design principles for aligning AI systems with human values

• Ethical and societal implications of HAIC

• Effects of HAIC on human autonomy and social norms

2. Algorithmic Adaptation and Robustness

• Enhancements to Reinforcement Learning from Human Feedback (RLHF)

• Technical frameworks for improving AI adaptability to human preferences

• Strategies for reducing bias and promoting fairness in AI decision-making

• Techniques for ensuring AI robustness across diverse contexts

3. Long-Term Societal Impact and Safety

• Implications of HAIC on governance, policy, and public decision-making processes

• Integration of AI alignment principles into socio-technological systems

• Reimagining AI safety in light of dynamic human-AI interactions

• Evaluating the impact of existing AI systems on future developments

4. Bidirectional Learning Beyond Performance Metrics

• Exploration of how prolonged human-AI interactions shape cognition and decision-making

• Revising evaluation metrics to assess AI systems through the lens of HAIC

• Investigating the interplay between human behavior and AI agency

5. Shaping Collective Behavior and Learning

• Examining AI's influence on group decision-making and consensus-building

• Addressing the role of AI in collaborative environments such as education and policy-making

• Understanding implicit biases formed through AI-mediated interactions

6. Dynamic Feedback Loops in Socially Impactful Domains

• Real-time feedback mechanisms in critical contexts (e.g., healthcare, education, criminal justice)

• Addressing unique demands of domain-specific AI-human interactions

• The role of AI in shaping outcomes in high-stakes environments

7. Socio-Technological Bias, Norms, and Ethics

• Critical analysis of how AI systems perpetuate or mitigate societal biases

• Examining ethical implications of AI feedback loops in decision-making

• Exploring the reshaping of social norms through AI interactions

• Addressing complexities of bias in the context of HAIC

We welcome submissions that provide innovative insights, case studies, empirical analyses, and theoretical contributions addressing these subjects. We welcome submissions of either (i) work that provides innovative insights, case studies, empirical analyses, and theoretical contributions addressing HAIC, (ii) position papers that make relevant arguments about HAIC, or (ii) expressions of interest in which prospective attendees describe their general background and interests in HAIC. Our aim is to facilitate interdisciplinary dialogue, foster collaboration, and advance the understanding of HAIC as a vital research area.

Tiny Papers

We are excited to host Tiny Papers at ICLR HAIC. 

This year, ICLR is discontinuing the separate “Tiny Papers” track, and is instead requiring each workshop to accept short (3–5 pages in ICLR format) paper submissions, with an eye towards inclusion; see ​​https://iclr.cc/Conferences/2025/CallForTinyPapers for more details. Authors of these papers will be earmarked for potential funding from ICLR, but need to submit a separate application for Financial Assistance that evaluates their eligibility. This application for Financial Assistance to attend ICLR 2025 will become available on https://iclr.cc/Conferences/2025/ at the beginning of February and close on March 2nd.

Please review the instructions for the Tiny Papers track at the Tiny Papers section of our site. 

To submit a "Tiny Paper" to ICLR-HAIC, select the Tiny Papers track on OpenReview.

Double blind reviewing

Submissions will be double blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names.  Having papers on arxiv is allowed per the dual submission policy outlined below.  

We use OpenReview to host papers and allow for public discussions that can be seen by all, comments that are posted by reviewers will remain anonymous. The program will include oral presentations and posters of accepted papers.

Authors can revise their paper as many times as needed up to the paper submission deadline. Changes to the paper will not be allowed while the paper is being reviewed. 

Submission Instructions

The full paper submission deadline is Feb 10, 2025 11:59pm AOE. Papers must be submitted using the conference submission system the link will be provided shortly.  Supplementary material is due at the same time as the main paper.

Paper length

The main text must take up at most 10 pages. This limit will be strictly enforced. Papers with main text on the 11th page will be desk rejected. The page limit applies to both the initial and final camera ready version.

We encourage authors to be crisp in their writing by submitting papers with no more than 9 pages of main text. We recommend that authors only use the longer page limit in order to include larger and more detailed figures. However, authors are free to use the pages as they wish, as long as they obey the page limits.

The list of references does not count towards the page limit, and unlimited additional pages are allowed for the bibliography/references.  

Authors may use as many pages of appendices (after the bibliography) as they wish, but reviewers are not required to read the appendix.

Style files and Templates

To prepare your submission to ICLR 2025, please use the LaTeX style files provided at:

https://github.com/ICLR/Master-Template/raw/master/iclr2025.zip

Submissions and reviews are both anonymous.

Reviewing Process

Submissions to ICLR are uploaded on OpenReview, which enables public discussion. Official reviews are anonymous and publicly visible. During the public discussion phase, anybody who is logged in can post comments that are publicly visible, or restrict visibility to reviewers and up, ACs and up, or just PCs. In addition, the author of a comment can decide to post anonymously or not. Login is required before posting any comment.

Once reviews have been posted, authors are encouraged to revise their paper until the paper submission deadline. Authors can participate in the discussion about their paper, as well as about any other paper submitted to the conference, at any time.

Full reviews alongside the acceptance decisions are posted are posted by Mar 5, 2025. Reviews are anonymous and publicly visible in OpenReview.

All submitted papers (accepted or rejected) will be deanonymized after the notification. The submissions and reviews will be released to the public.  

Code of Conduct

All ICLR participants, including authors, are required to adhere to the ICLR code of conduct (https://iclr.cc/public/CodeOfConduct). More detailed guidance for authors, reviewers, and all other participants will be made available in due course, and participation will require acknowledging and adhering to the provided guidelines.

Code of Ethics

All ICLR participants, including authors, are required to adhere to the ICLR Code of Ethics (https://iclr.cc/public/CodeOfEthics). All authors of submitted papers are required to read the Code of Ethics, adhere to it, and explicitly acknowledge this during the submission process. The Code of Ethics applies to all conference participation, including paper submission, reviewing, and paper discussion.

Dual Submission Policy

Submissions that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to this or other workshops, conferences or journals, are not allowed and violate our dual submission policy. However, papers that cite previous related work by the authors and papers that have appeared on non-peer reviewed websites (like arXiv) or that have been presented at workshops (i.e., venues that do not have publication proceedings) do not violate the policy. The policy is enforced during the whole reviewing process period. Submission of the paper to archival repositories such as arXiv is allowed during the review period.

The Use of Large Language Models (LLMs)

The use of LLMs is allowed as a general-purpose assist tool.  Authors and reviewers should understand that they take full responsibility for the contents written under their name, including content generated by LLMs that could be construed as plagiarism or scientific misconduct (e.g., fabrication of facts).  LLMs are not eligible for authorship.

Withdrawal Policy

Authors have the right to withdraw papers from consideration at any time until paper notification. Before the paper submission deadline, if an author withdraws the paper it will be deleted from the OpenReview hosting site. However, note that after the paper submission deadline, if an author chooses to withdraw a submission, it will remain hosted by OpenReview in a publicly visible "withdrawn papers" section. Like on arXiv, submissions to ICLR cannot be deleted or modified. Withdrawn papers will be de-anonymized immediately.