Vakaj, EdliraMihindukulasooriya, NandanaGaur, ManasKhan, Arijit2024-12-112024-12-112024-10-21Vakaj, Edlira, Nandana Mihindukulasooriya, Manas Gaur, and Arijit Khan. “Knowledge Graphs for Responsible AI.” In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 5596–98. CIKM ’24. New York, NY, USA: Association for Computing Machinery, 2024. https://doi.org/10.1145/3627673.3679085.https://doi.org/10.1145/3627673.3679085http://hdl.handle.net/11603/37048CIKM ’24, October 21–25, 2024, Boise, ID, USAResponsible AI is built upon a set of principles that prioritize fairness, transparency, accountability, and inclusivity in AI development and deployment. As AI systems become increasingly sophisticated, including the explosion of generative AI, there is a growing need to address ethical considerations and potential societal impacts of their uses. Knowledge graphs (KGs), as structured representations of information, can enhance generative AI performance by providing context, explaining outputs, and reducing biases, thereby offering a powerful framework to address the challenges of responsible AI. By leveraging semantic relationships and contextual understanding, KGs facilitate transparent decision-making, enabling stakeholders to trace and interpret the reasoning behind AI driven outcomes. Moreover, they provide a means to capture and manage diverse knowledge sources, supporting the development of fair and unbiased AI models. The workshop aims to investigate the role of knowledge graphs in promoting responsible AI principles and creating a cooperative space for researchers, practitioners, and policymakers to exchange insights and enhance their comprehension of KGs' impact on achieving responsible AI solutions. It seeks to facilitate collaboration and idea-sharing to advance the understanding of how KGs can contribute to responsible AI.3 pagesen-USAttribution 4.0 International CC BY 4.0https://creativecommons.org/licenses/by/4.0/UMBC Ebiquity Research GroupText