First Call for Papers and Shared Tasks Participation: AIGCTrustDetect@ COLING 2027 - The 1st Workshop on AI-Generated Content Detection and Trustworthy Governance
We invite original, unpublished research on secure, explainable and robust governance of AI-generated content. AIGCTrustDetect goes beyond binary detection toward full-stack governance, and especially welcomes work with clear potential for generalization and cost-effective real-world deployment.
Accepted papers will be presented at the workshop, held as part of COLING 2027 in Macau.
TOPICS OF INTEREST
Topics include, but are not limited to:
* Reliable and explainable detection. Detection methods for AI-generated text: zero-shot and supervised detectors, watermarking and provenance signals, stylometric and statistical features; interpretability, calibration and uncertainty estimation; attribution to specific models or generators; evaluation protocols, benchmarking and error analysis; decisions that humans can verify and contest. * Detection across modalities. AI-generated content detection beyond text: speech, image, video and multimodal content, including deepfakes and synthetic media, and the transfer of detection techniques across modalities. * Detection in complex generation scenarios. Mixed human-AI authorship and LLM-refined text; paraphrasing, translation and other obfuscation; adversarial attacks on detectors and deliberate evasion; robustness and generalization across models, domains, prompts and decoding strategies; real-time and large-scale detection systems. * Detection and governance in agentic workflows. Content produced by LLM agents and tool-using systems: provenance and audit trails across multi-step generation, detection and attribution of AI-generated code, reliability of agent-written reports, and human oversight of automated content pipelines. * Cross-lingual generalization. Multilingual and cross-lingual detection; transfer and low-cost adaptation to low-resource languages; language-agnostic signals; multilingual datasets and benchmarks. * Linguistic perspectives on AI-generated text. Syntactic, semantic, discourse and pragmatic signatures of machine writing; corpus-based and stylometric comparison of human and machine text; register, genre and stylistic variation across languages; linguistic typology and its implications for detection; and human perception of AI-generated language. * Hallucination, reliability assessment and factuality verification. Whether generated content is accurate, supported and proportionate: hallucination detection, evidence grounding and claim verification, evidence-statement mismatch and overclaim, fake news and misinformation, reliability assessment of generated text, and grounding and citation fidelity in retrieval-augmented generation. * AI assistance in scholarly writing and peer review. Detection and disclosure of AI-generated manuscripts and reviews, reliability of LLM-as-a-judge evaluation, and governance of AI assistance across the research pipeline. * Hierarchical and differentiated governance. Governance itself as a research problem: tiered frameworks and new tasks that formalize proportionate response across settings with different stakes, from education to journalism to content platforms; disclosure, provenance standards and policy; fairness and disparate impact of detectors, safeguards against false accusation; ethical, legal and societal implications of AI-generated content. * Synthetic data and training-set governance. Detection and labeling of AI-generated content in training corpora, reliability of synthetic data, contamination and deduplication, and implications of recursive training for future detectors. * Datasets, resources and tooling. Corpora, benchmarks, evaluation tools and open-source infrastructure supporting any of the directions above.
Key dates:
* Submission deadline: 10 January 2027 (Anywhere on Earth, UTC-12) * Notification of acceptance: 10 February 2027 * Workshop date: One day in May 2027 (exact date TBC)
Workshop website: https://nlp2ct.github.io/COLING-2027-Workshop-AIGCTrustDetect/