Trustworthy Machine Learning for Web Information Systems

a special topic track of the 25th International Web Information Systems Engineering conference (WISE 2024)
2-5 December 2024, Qatar University, Doha, Qatar.

The special track “Trustworthy Machine Learning for Web Information Systems” aims to address the pivotal challenges and opportunities that arise at the intersection of trustworthy machine learning and web information systems. As machine learning becomes increasingly integral to web technologies, ensuring these systems are privacy-preserving, robust, secure, fair, and transparent is paramount. This track seeks to bring together a diverse array of research contributions that explore innovative approaches to enhancing the trustworthiness of machine learning based web information systems. Topics of interest include, but are not limited to, privacy enhancing technologies, robustness against adversarial attacks, fairness and bias mitigation, transparency and explainability in the context of web information systems. We expect to gather researchers from academia and industry to present the latest advances and future directions in designing, deploying secure and trustworthy machine learning algorithms, techniques, and protocols for real-world web applications, services, and systems. In this track, we solicit research papers and position papers to investigate best practices, new methods, and secure design principles. Ultimately, the goal is to advance the state of the art in creating machine learning based web systems that are not only technically proficient but also ethically sound, socially responsible, and trusted by users and stakeholders alike.


Topics of Interest

Areas of interest include (but not limited to):

Submission Guidelines

Papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer's LNCS format format and should not exceed 15 pages, including all text, figures, references, and appendices. Submissions not conforming to the LNCS format, exceeding 15 pages, or being obviously out of the scope of the conference, will be rejected without review. Information about the Springer LNCS format can be found at Springer. Three to five keywords characterizing the paper should be indicated at the end of the abstract.

All submissions must go through EasyChair system via Easychair.


Dual Submission

It is not appropriate to submit papers that are identical (or substantially similar) to versions that have been previously published, accepted for publication, or submitted in parallel to other conferences or journals. Such submissions violate our dual submission policy, and the organizers have the right to reject such submissions, or to remove them from the proceedings.

Changes of Title / Abstract / Authorship

Authors should include a full title for their paper, as well as a complete abstract by the abstract submission deadline. Submission titles should not be modified after the abstract submission deadline, and abstracts should not be modified by more than 50% after the abstract submission deadline. Submissions violating these rules may be deleted after the paper submission deadline without reviewing. The author list at the submission deadline will be considered final, and no changes in authorship will be permitted for accepted papers.

Reviewing Criteria

Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact. Reproducibility of results and easy availability of code will be taken into account in the decision-making process.

Important Dates


Please note that for every accepted paper, it is required that at least one person registers for the conference and presents the paper. All accepted papers will be included in the proceedings published as Springer’s LNCS series.

Program Committee

  • Anjia Yang, Jinan University
  • Guangdong Bai, The University of Queensland
  • Hagen Lauer, Technische Hochschule Mittelhessen - University of Applied Sciences
  • He Zhang, Monash University
  • Jin Hong, The University of Western Australia
  • Leo Zhang, Griffith University
  • M.A.P. Chamikara, CSIRO’s Data61
  • Niklas Bunzel, Fraunhofer SIT / ATHENE
  • Shangqi Lai, CSIRO’s Data61
  • Sharif Abuadbba, CSIRO’s Data61
  • Siqi Ma, The University of New South Wales
  • Viet Vo, Monash University
  • Xiaoning Liu, RMIT University
  • Yaxing Chen, Northwestern Polytechnical University
  • Yifeng Zheng, Harbin Institute of Technology
  • Yu Guo, Beijing Institute of Technology
  • Yuan Zhang, University of Electronic Science and Technology of China
  • Ziyao Liu, Nanyang Technological University