Solana Larsen- Who Has Power Over AI?
Explore power dynamics in AI through Mozilla's Internet Health Report, featuring global perspectives on shifting control and fostering diverse AI applications for a more inclusive future.
Are Foundation Models Castles in the Air?
Explore the trustworthiness and effectiveness of foundation models in AI with Stanford professor Percy Liang, examining their potential and limitations for human-computer interaction and continuous learning.
Embracing Ethical AI: A Proactive Approach to Cyber Risk Management and Regulatory Compliance
Develop proactive strategies for ethical AI deployment, addressing cyber risks and regulatory compliance while gaining a competitive edge in the era of Generative AI.
Trustworthy AI for MLOps
Explore strategies for implementing trustworthy AI in MLOps, addressing operational challenges, and fostering transparency to turn AI into a business advantage.
Making AI Systems Trustworthy Through Intuitive Design and Human Interaction
Explore key technical approaches for building trustworthy AI systems, focusing on intuitiveness, interaction, explainability, and human domain knowledge integration for more reliable and transparent artificial intelligence.
What's New in Trustworthy AI in Julia (Taija) - JuliaCon 2024
Discover the latest developments in Julia's Trustworthy AI ecosystem, exploring advancements in model explainability, algorithmic recourse, uncertainty quantification, and Bayesian deep learning through the Taija framework.
Machine Learning in Healthcare - From Interpretability to Human-Machine Partnership
Exploring interpretable machine learning in healthcare, proposing a framework for developing models based on user needs, and discussing potential applications for understanding and empowering human decision-making.
AI One Can Trust
Explore trustworthy AI principles and applications with Dr. Biplav Srivastava, focusing on ethical considerations and practical implementations in various domains.
Algorithmic Fairness and Individual Probabilities
Explore algorithmic fairness and the concept of individual probabilities, examining their implications for AI ethics and decision-making processes in machine learning applications.
Building Machine Learning Systems for the Era of Data-Centric AI
Explore the evolution of ML systems beyond model training, focusing on data quality, trustworthiness, and regulatory compliance through systematic data debugging and cleaning approaches.
Guiding AI Governance Evolution: Traditional to Generative AI
Explore the evolving landscape of AI governance and learn how Large Language Models introduce new challenges and requirements for responsible AI implementation in enterprise settings.
Trustworthy AI and Cybersecurity Perspectives on Large Language Models - Lecture 32
Explore cybersecurity challenges and trustworthiness concerns in Large Language Models through expert insights on risk assessment, safety measures, and ethical AI development practices.
Building Trustworthy AI with Common Sense - Lecture
Explore building trustworthy AI with common sense for robust decision-making, discovery, and misinformation tackling. Learn about CARE principles and neuro-symbolic architectures for explainable, adaptive AI systems.
Governance for Trustworthy AI
Explore governance frameworks for developing trustworthy AI systems, focusing on ethical considerations and responsible implementation strategies.
Human-Centered AI: Challenges and Governance in News Automation
Explore the synthesis of AI and human-centered thinking with experts discussing challenges, governance, and innovation in developing reliable and trustworthy Human-Centered AI systems.
Memorization in Machine Learning
Explore information-theoretic methods for trustworthy machine learning, focusing on memorization techniques and their implications for model reliability and data privacy.
Trustworthy Machine Learning: Challenges and Frameworks
Exploring the security challenges in machine learning, this talk discusses strategies for building trustworthy AI systems by analyzing data influence, addressing vulnerabilities, and enhancing interpretability.
Majorizing Measures, Codes, and Information
Explore the connections between majorizing measures, coding theory, and information theory in machine learning, with insights from Maxim Raginsky.
How to Build Trustworthy AI with Open Source
Explore open-source tools for building trustworthy AI systems, focusing on robustness, fairness, and explainability to ensure ethical and responsible AI development.
Trustworthy AI in Network Operations
Explore symbolic reinforcement learning, formal verification, and explainability in AI-based models for network operations, addressing challenges and advancements in trustworthy AI.
How Can We Make Trustworthy AI
Explore strategies for developing reliable and ethical AI systems, focusing on transparency, accountability, and responsible implementation in various domains.