INTRODUCTION

The way research and business manage and utilize knowledge is undergoing a significant transformation, driven by Artificial Intelligence (AI). Deep learning and machine learning are emerging as a powerful tool for optimizing such knowledge management systems, leading to a more informed and productive development. AI offers a unique solution for organizations struggling with information overload and inefficient knowledge transfer. These AI models can significantly improve data management and utilization. Imagine an AI-powered system that streamlines onboarding processes, provides precise answers to various queries, and even captures the valuable tacit knowledge (implicit skills and expertise) often residing somewhere else. AI bridges the gap between explicit knowledge (easily documented information) and tacit knowledge, fostering a more comprehensive and accessible knowledge base. However, such AI systems solicit a trustworthy and responsible solution that can cater the potential misuse and malfunction. In this workshop, we aim to gather researchers and engineers from academia and industry to discuss the latest advances for trustworthy and responsible AI solutions for information and knowledge management systems.

SUBMISSION

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

IMPORTANT DATES

**(All deadlines are at 11:59 pm in the Anywhere on Earth timezone.)**
Paper submission deadline: August 4th, 2024
Notification to Authors: August 30st, 2024
Registration: TBD
Conference date: October 25st, 2024

WORKSHOP PROGRAM

To be updated

INVITED SPEAKERS

To be updated

ORGANIZATION COMMITTEES

PROGRAM COMMITTEES

To be updated

WEB CHAIR

JiawenWen

Jiawen Wen

The University of Sydney

LinghanHuang

Linghan Huang

The University of Sydney

CONTACT US

Point of Contact:

Huaming Chen, The University of Sydney

Email: huaming.chen@sydney.edu.au