Responsible AI is built upon 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, Knowledge Graphs facilitate transparent decision-making processes, 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 (KGs) 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

We invite submissions of original research, case studies, and position papers on topics related to Knowledge Graphs and their applications in advancing Responsible AI. The workshop explores the intersection of Knowledge Graphs and ethical considerations in AI development. Submissions may include, but are not limited to, the following topics:

Knowledge Graphs for Bias Mitigation:

• Techniques and methodologies for using Knowledge Graphs to identify and mitigate biases in AI models.
• Case studies demonstrating the successful application of Knowledge Graphs in addressing bias challenges.

Interpretability and Explainability:

• Approaches to enhancing the interpretability and explainability of black-box AI models through integrating Knowledge Graphs.
• Evaluating the effectiveness of Knowledge Graphs in making AI decision-making processes more transparent.

Privacy-Preserving Knowledge Graphs:

• Methods for constructing Knowledge Graphs that prioritize privacy and comply with data protection regulations.
• Applications of Knowledge Graphs in privacy-preserving AI systems.

Fairness in AI with Knowledge Graphs:

• How Knowledge Graphs Contribute to Ensuring Fairness in AI Applications.
• Techniques for using Knowledge Graphs and their embeddings to identify and rectify unfair biases in AI models.

Ethical Considerations in Knowledge Graph Construction:

• Ethical challenges in the creation and maintenance of Knowledge Graphs.
• Best practices for ensuring responsible and ethical Knowledge Graph development.
• Real-world applications of Knowledge Graphs in Responsible AI.

Integration of Large Language Models (LLMs) and Knowledge Graphs (KGs):

• Enhancing LLMs’ accuracy and consistency, reducing hallucinations and harmful content generation, fake news detection, fact-checking, etc., with knowledge-grounded techniques.
• Enhancing the interoperability of KG downstream tasks through LLMs’ natural language interfaces, transferability, and generalization capacity.

Camera Ready Submission: September 7, 2024

We welcome original research papers in four types of submissions:

1. Full research papers (9 pages including references)
2. Position papers(4-6 pages) including references)
3. Short papers (6 pages including references)
4. Demo (4-6 pages including references) papers

A skilled and multidisciplinary program committee will evaluate all submitted papers, focusing on the originality of the work and its relevance to the workshop's theme. Acceptance of papers will adhere to the CIKM 2024 Conference Template and undergo a double-blind review process. More details regarding submission can also be found at https://cikm2024.org/call-for-papers/. Selected papers will be presented at the workshop and published as open-access in the workshop proceedings through CEUR, where they will be available as archival content.

1. Dual submission policy: This workshop welcomes ongoing and unpublished work but will also accept papers that are under review or have recently been accepted at other venues.
2. OpenReview Moderation Policy: Authors are advised to register on OpenReview using their institutional email addresses when submitting their work to "Knowledge Graphs for Responsible AI". Using personal email addresses, such as "gmail.com," may delay up to two weeks for Openreview to verify and allow the submission. This could cause inconvenience during the busy submission period.