Abstract
The rapid deployment of Generative and Agentic AI systems-ranging from large language models to autonomous agents-has created a critical need for rigorous and trustworthy evaluation methodologies. As these models influence real-world decision-making, traditional performance metrics alone fall short in capturing issues of safety, ethical alignment, misinformation, and human-centered usability. This workshop addresses these challenges by fostering interdisciplinary discussions and innovations in evaluation strategies that go beyond conventional benchmarks. Topics include holistic and multi-perspective assessments, scalable evaluation pipelines, reasoning and goal alignment in agentic behavior, misinformation detection, cross-modal generation, and trust calibration. By advancing robust, user-centric, and societally grounded evaluation practices, this workshop contributes to expanding KDD's methodological frontier into the emerging domain of responsible AI systems.
| Original language | English (US) |
|---|---|
| Title of host publication | KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
| Publisher | Association for Computing Machinery |
| Pages | 6286-6287 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798400714542 |
| DOIs | |
| State | Published - Aug 3 2025 |
| Externally published | Yes |
| Event | 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 - Toronto, Canada Duration: Aug 3 2025 → Aug 7 2025 |
Publication series
| Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|---|
| Volume | 2 |
| ISSN (Print) | 2154-817X |
Conference
| Conference | 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 8/3/25 → 8/7/25 |
Bibliographical note
Publisher Copyright:© 2025 Owner/Author.
Keywords
- bias and fairness
- cross-modal evaluation
- ethical considerations in ai
- evaluation metrics
- generative ai models
- trustworthiness
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