Guardrails for Trustworthy AI: Balancing Innovation and Responsibility
Explore strategies for implementing trustworthy AI, focusing on fairness, accountability, and ethical use of large language models. Learn to balance innovation with responsible AI practices.
Contextual Stochastic Bandits with Budget Constraints and Fairness Application
Explore contextual stochastic bandits, budget constraints, and fairness applications in machine learning. Learn strategies for balancing exploration and exploitation in reward maximization.
Tackling Fairness, Change, and Polysemy in Word Embeddings
Explore fairness, change, and polysemy in word embeddings with Felipe Bravo's presentation on cutting-edge research in natural language processing and machine learning.
Navigating the Ethical Horizon: Pioneering Responsible AI with the Generative AI Commons
Explore responsible AI's role in ethical tech development, focusing on the Generative AI Commons framework for transparency, fairness, and inclusivity across the AI lifecycle.
AI for Inclusive Society - Responsible AI
Explore responsible AI principles for inclusive, trustworthy solutions. Learn about AI fluency, shared accountability, and intentional inclusivity to avoid unintentional exclusion in AI development and deployment.
Evaluation Beyond Relevance - Accounting for Credible, Correct and Fair Information
Explore evaluation methods for information systems that address misinformation, fairness, and bias beyond traditional relevance metrics, with practical insights into quantitative measurement approaches.
Online Learning for Network Resource Allocation
Explore advanced online learning techniques for optimizing network resource allocation, focusing on caching strategies, inference delivery networks, and fairness in unpredictable environments.
A Theory of Multi-objective Machine Learning
Explore multi-objective learning as a unifying paradigm for robustness, collaboration, and fairness in machine learning, with technical tools and empirical evidence.
Fostering Cooperation via Fairness in AI Systems
Explore fairness in AI decision-making, addressing efficiency, elicitation, and incentives. Learn theoretical foundations for fairness in voting, resource allocation, and machine learning.