Governing AI with AI: Combating Generative Forgery and Safeguarding Academic Integrity in Social Computing

The rapid advancement of Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) and advanced diffusion models, has fundamentally transformed the landscape of knowledge production and dissemination. While these technologies offer unprecedented assistance in scientific research, they have concurrently triggered a profound behavioral crisis in academic publishing. The barrier to generating hyper-realistic synthetic data, fabricated microscopic images, and AI-generated texts has been drastically lowered. This technological shift has altered the behavioral dynamics of authors and posed an existential threat to traditional peer-review processes and editorial decision-making.

As traditional human oversight and editors armed with conventional tools become overwhelmed by the volume and sophistication of modern academic misconduct, there is an urgent need to explore the societal impacts of these deceptive behaviors and rebuild trust in scientific literature. This special session proposes the paradigm of "Governing AI with AI" to address these challenges. By leveraging advanced computational models to audit and regulate AI-generated content, we can establish a new equilibrium in academic governance.

This session aims to bridge the gap between artificial intelligence researchers, computational social scientists, and academic publishing professionals (e.g., editors and publishers). We seek to explore the behavioral shifts in academic misconduct in the GenAI era, share cutting-edge detection frameworks, and discuss the human-AI interaction in editorial workflows. The primary objective is to foster a collaborative ecosystem where data-driven detection algorithms and human editorial expertise synergize to safeguard the credibility of science.

The special session invites submissions addressing (but not limited to) the following areas:

  • Methodological Innovations in AI Detection:
    • Detection of AI-generated text, images, and cross-modal forgeries in academic publishing.
    • Robust detection frameworks for traditional and AI-assisted image manipulation (e.g., splicing, reuse, targeted editing, and rendering).
    • End-to-end AI governance platforms and automated tools for editorial workflows.
    • Construction of benchmarking datasets and evaluation metrics for academic forgery detection.
  • Behavioral and Social Dynamics of Academic Misconduct:
    • Analyzing the behavioral shifts of authors, reviewers, and publishers in the GenAI era.
    • Social network analysis of academic misconduct (e.g., paper mills, citation cartels, and organized fraud).
    • The psychological and sociological drivers of technology-assisted academic fraud.
  • Ethics, Trust, and Human-AI Collaboration:
    • Human-in-the-loop AI systems for academic publishing, peer review, and scientific auditing.
    • Ethical frameworks and policy-making for AI-assisted scientific writing and publishing.
    • Societal impacts of academic misinformation and strategies to rebuild trust in science.
    • Explainable AI (XAI) for transparent and trustworthy fraud detection in high-stakes editorial decisions.
  • Surveys and Future Directions:
    • Comprehensive surveys on the evolution of AI-driven detection technologies, including algorithms and frameworks for identifying text, image, and multimodal forgeries in scientific literature.
    • Systematic reviews and taxonomies of AI-generated content (AIGC) in academic publishing and its long-term impact on the scientific ecosystem and research culture.
    • Benchmarking and evaluation protocols for developing sustainable, cross-domain datasets and metrics for the continuous auditing of evolving generative models.
    • Speculations on "Governance-by-Design" paradigms, exploring the integration of decentralized AI and automated oversight systems for next-generation scientific integrity.

submission instruction

Please use the below links to download the Springer template and to submit your work.

The format of Research Papers should be suitable for original research, which is completed work at the time of submission and, regardless of the length of the paper, is a self-sufficient scientific contribution. Selected papers will be invited for submission to journals.

All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to BESC 2026, originality, significance and clarity.

Please note:

  • Authors must use Springer LNCS/LNAI manuscript submission guidelines and formatting template for their submissions and each paper must be at least 6 pages and no longer than 16 pages in length (including references).
  • All papers must be submitted electronically through the paper submission system in PDF format only.
  • Paper review will be double-blind, and submissions not properly anonymized will be desk-rejected without review.
  • Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings.
  • Papers must be clearly submitted in English and will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation.
  • Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.
  • Accepted papers will be included in the proceedings of BESC 2026 and indexed by EI Compendex, ISI, Scopus, DBLP, ACM Digital Library, Google Scholar and other A&I services.
  • Top quality papers after presented in the conference will be selected for extension and publication in several special issues of international journals (TBC), including:
    • IEEE Transactions on Computational Social Systems (IEEE)
    • CCF Transactions on Pervasive Computing and Interaction (Springer)
    • EURASIP Journal on Image and Video Processing (Springer)
    • World Wide Web Journal (Springer)
    • Social Network Analysis and Mining (Springer)
    • Human-Centric Intelligent Systems (Springer)
    • ACM Transactions on Social Computing (ACM)
    • Natural Language Processing (Elsevier)
    • Health Information Science and Systems (Springer)
    • Web Intelligence (IOS Press)
  • Papers that are substantially or entirely generated by generative AI tools are not permitted. The use of generative AI as an assistive tool (e.g., for language editing) is allowed, provided that such use is clearly and explicitly disclosed in the paper. Authors remain fully responsible for the content, originality, and integrity of their submissions.

Contact

Program Chairs
  • Prof. Haihong E, Beijing University of Posts and Telecommunications, China
  • Prof. Xiaodong Qiao, Beijing Wanfang Data Co., Ltd., China
Program Committee Members
  • Prof. Guangjian Li, Peking University, China
  • Prof. Zhixiong Zhang, The National Science Library, Chinese Academy of Sciences, China
  • Prof. Haofen Wang, Tongji University, China
  • Prof. Dongping Gao, Institute of Medical Information, Chinese Academy of Medical Sciences (CAMS)
  • Prof. Daiqing Yang, Center for Scientometrics and Evaluation, Institute of Scientific and Technical Information of China (ISTIC)
  • Associate Prof. Jianhua Liu, Beijing Wanfang Data Co., Ltd., China
  • Dr. Dayong Wu, iFLYTEK, China
  • Dr. Huanyong Liu, The 360 AI Research Institute, China
  • Dr. Zichen Tang, Beijing University of Posts and Telecommunications, China
  • Dr. Junpeng Ding, Beijing University of Posts and Telecommunications, China
  • Dr. Wanting Wang, Beijing University of Posts and Telecommunications, China