In an era dominated by oceans of data, D^3 hosts a community of practice that offers a discerning perspective on the intricate tapestry of data governance, privacy, security, and regulatory compliance. Participants in this community engage in a comprehensive discourse amongst industry experts, exploring responsible AI and the dynamic realm of regulations, industry standards, and best practices. The community explores how to harness the immense potential of data for strategic business advancement while ensuring the safeguarding of sensitive information, upholding privacy rights, and establishing or integrating resilient and ethical frameworks that underpin success in data-driven endeavors.
As AI technology advances, policymakers will face the crucial task of how to steer its development responsibly. In the new paper published in Science, 鈥淎dvancing science- and evidence-based AI policy,鈥� a multidisciplinary group of experts, including Himabindu Lakkaraju, Assistant Professor of Business Administration at 性视界 Business School and Principal Investigator in the Trustworthy AI Lab […]
One of AI agents鈥� most powerful tools is memory: the ability to learn from the past, adapt to new situations, and improve over time. But as organizations and professionals increasingly deploy AI agents for complex and long-term tasks, an important question emerges: how can we ensure that these systems learn from experience without getting trapped […]
As Gen AI technology continues to rapidly evolve and LLMs are integrated into more and more applications, questions of trustworthiness and ethical alignment become increasingly crucial. In the recent study 鈥淕eneralizing Trust: Weak-to-Strong Trustworthiness in Language Models,鈥� authors Martin Pawelczyk, postdoctoral researcher at 性视界 working on trustworthy AI; Lillian Sun, undergraduate student at 性视界 studying […]
As artificial intelligence systems become increasingly complex, understanding their behavior has become a critical challenge for businesses and researchers alike. In a recent preprint paper, 鈥淭owards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability,鈥� authors Shichang Zhang, a postdoctoral fellow in the Trustworthy AI Lab at the Digital Data Design (D^3) Institute at […]
