Trustworthy Knowledge Discovery and Data Mining (TrustKDD)

The explosion of data and the widespread adoption of AI techniques, especially the success of foundation models and generative AI, have transformed knowledge discovery and data mining (KDD), making them integral to real-world decision-making. For both traditional AI methods and generative AI, issues such as data noise, algorithmic bias, lack of interpretability, and privacy concerns can significantly impact the quality and reliability of extracted knowledge, thereby affecting downstream decision-making. This workshop aims to bring together researchers and practitioners from information and knowledge management, data mining, and intelligent systems to explore trustworthy KDD across diverse settings in the generative AI era. We welcome contributions on robust data preprocessing, explainable learning algorithms, bias detection and mitigation, secure and privacy-preserving mining, trustworthy knowledge graph construction, resource-efficient deployment, alignment of foundation models, and applications for social good. Special emphasis is placed on emerging challenges posed by large-scale, pre-trained models in dynamic, multi-source, and user-centric environments. By fostering dialogue between traditional KDD approaches and innovations in the foundation model era, TrustKDD seeks to advance trustworthy methodologies that align with CIKM's mission of developing reliable, scalable, and intelligent information and knowledge systems.