Mitigating Bias in Models with SHAP and Fairlearn
Learn techniques to identify and mitigate bias in machine learning models using SHAP and Fairlearn, enhancing fairness and transparency in AI systems.
Flagging and Fixing Bias in Machine Learning
Explore techniques to identify and mitigate bias in machine learning models, enhancing fairness and accuracy in AI applications.
The Promise and Peril of Artificial Intelligence - Ethical Regulation and Implementation
Explore AI's societal impact, ethical challenges, and regulatory needs with insights from government and tech leaders on implementation, innovation, and global competitiveness.
Trustworthiness Framework - Metrics and Assessment Tools
Explore metrics and assessment tools for evaluating AI system trustworthiness, enhancing understanding of responsible AI development and deployment.
Fairness in Commercial Face Recognition Algorithms - Session 3
Explore fairness issues in commercial facial recognition systems, examining biases and ethical implications for diverse populations.
AI and Algorithmic Decision Making: Human Intuition vs Machine Capabilities
Explore AI's role in decision-making with Ray Dalio and Garry Kasparov, discussing its potential and limitations compared to human intuition in various domains.
A Better World with AI - Samsung's Vision for the Future
Explore AI's impact on Samsung's innovation, global opportunities, and ethical considerations in data growth, sensors, computing, and mobility.
Privacy Preserving AI in Cryptography - Round Table Discussion
Explore privacy-preserving AI techniques in this roundtable discussion from the Glowing Hot Topics in Cryptography workshop, focusing on the intersection of cryptography and artificial intelligence.
Is ChatGPT and Other AI the Enemy of Diversity?
Explore AI's impact on BIPOC communities, addressing potential benefits and risks. Learn strategies to create fair, unbiased, and inclusive AI systems for a more equitable future.
The Search for Transparency and Accountability in the Age of AI - RAI and XAI as Essential Tools
Explore responsible AI and explainable AI as essential tools for building ethical, transparent, and trustworthy systems. Uncover techniques to interpret complex models and foster accountability in AI development.
Skynet or Star Trek - The Future of AI
Experts discuss AI's future, addressing ethical concerns, security issues, and economic impact while exploring potential scenarios ranging from dystopian to utopian outcomes.
How to Be a Good Parent to AI
Explore how parenting principles can guide responsible AI development for societal benefit, drawing insights from Prof. Chirag Shah's research on intelligent information systems.
Trust Cards for AI
Explore AI trust challenges and solutions with Trust Cards, a tool for enhancing transparency and accountability in AI systems development and deployment.
Finding Our Place in the Age of AI
Exploración del impacto de la IA en nuestras vidas y carreras, destacando oportunidades y desafíos. Un joven pionero comparte su experiencia y visión sobre el futuro de la inteligencia artificial.
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.
Gen-AI ChatGPT: Applications and Implications
Exploración de ChatGPT y la IA generativa: capacidades, aplicaciones y implicaciones en diversos campos, incluyendo matemáticas e investigación académica.
Defining Open AI - Interactive Workshop
Interactive session exploring the definition of "open" in AI/ML systems. Participants contribute to shaping principles for collaborative AI development, reviewing a draft definition in real-time.
The Impact of AI Regulation on Open Innovation - Panel Discussion
Experts discuss how AI regulations in different jurisdictions may affect open innovation, comparing approaches and exploring implications for developers and researchers.
AI with a Human Perspective - What We See and What We Value
Explore AI's evolution in visual intelligence, from neuroscience-inspired algorithms to addressing ethical concerns and developing human-centered applications in healthcare and robotics.
Cultivating Kinder, Gentler, and More Democratic AI
Explore the Paul English Applied AI Institute's mission to democratize AI ethically and empower students at UMass Boston, fostering innovation for public good and equal access to AI technologies.
Algorithmic Fairness, Loss Minimization and Outcome Indistinguishability
Explore algorithmic fairness, loss minimization, and outcome indistinguishability in machine learning. Discover how these concepts address fairness issues and enable flexible predictor optimization across various constraints.