Authors are invited to submit original, full-length research papers that are not previously published, accepted to be published, or being considered for publication in any other forum. Full-length papers should satisfy the standard requirements of top-tier international research conferences.
Manuscripts should be submitted to CIKM 2024 Easychair site in PDF format, using the 2-column ACM sigconf template, see https://www.acm.org/publications/proceedings-template. Full papers cannot exceed 9 pages, including an appendix, plus unlimited references (paper content is limited to 9 pages, that means that if you have an appendix, then it should be included within that page limit. It is also ok if you do not have an appendix and instead 9 pages of content). The review of manuscripts will be double-blind, and submissions not properly anonymized will be desk-rejected without review.
Papers that include text generated from a large-scale language model (LLM), such as ChatGPT, are prohibited unless this produced text is presented as a part of the paper’s experimental analysis. AI tools may be used to edit and polish authors’ work, such as using LLMs for light editing of their text (e.g., automate grammar checks, word autocorrect, and other editing of author-written text), but text “produced entirely” by generative/AI models is not allowed.
All the papers should be submitted using Easychair website:
https://easychair.org/my/conference?conf=traicikm2024.
At least one author of each accepted paper must register to present the work on-site in Boise, Idaho, USA, as scheduled in the conference program.
Selected high quality papers will be recommended for publication at high-impact international journals such as IEEE trans, after further extensions and revisions.
We welcome submission from different aspects of trustworthy and responsible AI for information and knowledge management systems (IKMS), including but not limited to
- Theoretical understanding of trustworthy machine learning, such as trustworthy graph learning, trustworthy federated learning and so on
- Trustworthy AI-supported knowledge management
- Trustworthy and responsible AI for search and recommendation
- Misinformation detection
- AI ethics and its impacts on knowledge management
- Reflective applications/demos of trustworthy ML for knowledge management