Policy on Use of ChatGPT or Similar Models
Text, images or any material generated from foundation models (LLMs, VLMs etc.), such as ChatGPT, must be clearly marked where such tools are used for purposes beyond editing the author’s own text. Please carefully review the April 2023 ACM Policy on Authorship before you use these tools. The SIGCHI blog post describes approaches to acknowledging the use of such tools and we refer to it for guidance. Note that the LaTeX template will default to hiding the Acknowledgements section while in review mode – please make sure that any LLM disclosure is available in your submitted version for review. While we do not anticipate using tools on a large scale to detect LLM-generated text, we will investigate submissions brought to our attention and desk reject papers where LLM use is not clearly marked.
Furthermore, HRI follows the ACM Policy on Authorship. Please carefully review this policy. Any AI system, including Generative Models, such as ChatGPT, BARD, or DALL-E, do not satisfy the criteria for authorship of papers and, as such, also cannot be used as a citable source in papers published by ACM and IEEE. Authors assume full responsibility for content, including checking for plagiarism and veracity of all text.
Anonymizing Submissions
The HRI conference full papers, as well as other submission types such as Short Contributions, alt.HRI, or Late Breaking Reports, follow a double-blind review process, requiring authors to prepare an anonymized submission.
To prepare an anonymized submission, authors are expected to remove author and institutional identities from the cover page, the acknowledgments section, and the PDF meta-data. Institution information should also be removed from the body of the text. For instance, use “…participants were recruited from a university campus” instead of “…participants were recruited from .”
Additionally, we recommend removing marks that identify institutional affiliation from images and supplementary videos (e.g., institutional attire, logos) as much as possible. However, pictures of robots used and study setup, in general, do not need to be anonymized, even if the robot uniquely identifies your group.
We ask that authors leave citations to their prior work un-anonymized in order to provide a more comprehensive review of prior work including the authors’ own research. Authors citing their own prior work should discuss it in the third person. For instance, instead of “Our prior work [6]…” use “Prior work by [6]…”
The checklist below summarizes the anonymization procedure.
Author checklist for anonymizing submissions:
- Remove author and institution information from the cover page as well as from acknowledgments section.
- Clear all meta-data in a word processor or PDF viewer/editor.
- Replace institution information in the body of the text with generic identifiers.
- Use the third person for citations to own work.
- Remove marks for institutional affiliation from images and supplementary materials (as much as possible).
- Ensure all artifacts, appendices, software, etc are fully anonymized.
Posting articles publicly online (e.g., own website, arXiv)
To maintain the double-blind review process, we request authors refrain from posting their articles online until the final notification date.