2026 6th International Conference on Big Data, Artificial Intelligence and Risk Management（ICBAR 2026）

As a leading role in the global megatrend of scientific innovation, We has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. Such endeavors are making new contributions to the globalization and creating a community of shared future.

2026 6th International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR 2026) will be held in Macau,China on December 10-12, 2026. Since the beginning of the 21st century, the progress of big data, artificial intelligence and risk management science has promoted the prosperity and development of the society and economy. Many universities and enterprises have developed many related technologies and products, and have achieved fruitful academic results and application transformation. The purpose of the conference is to provide experts, scholars, engineering technicians, and R&D personnel engaged in big data, artificial intelligence and risk management technology with a way to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote A platform for cooperation in the industrialization of academic achievements. The conference sincerely invites experts from domestic and foreign universities, scientific research institutions, scholars, business people and other relevant personnel to participate in the conference.

Xidian University, China Distinguished Professor, IEEE Fellow, Member of Finnish Academy of Science and Letters, Member of European Academy of Sciences and Arts

Abstract: While Artificial Intelligence (AI) is contributing to the advancement of human society, it also presents us with new challenges. Its trustworthiness is worthy of in-depth exploration. This talk elucidates the aids of AI for trust and indicates the problems of AI’s trustworthiness, especially potential attacks on AI and the factors that impact AI trust. I will summarize various attacks suffered by AI in its life cycle. In particular, I will introduce recent research achievements of my team, including LLM-aided trust evaluation, GNN-based robust and context-aware trust evaluation models, Large Language Model’s trust problems, a stealthy and practical audio backdoor attack with limited knowledge, LLM enhanced GNN robustness against poisoning attacks. Finally, several insights are proposed regarding AI trust management, especially an agentic system’s trust management.