The Ethical Algorithm
Explore ethical challenges in algorithmic design, focusing on data privacy and fairness. Learn about differential privacy, anonymization techniques, and defining fairness in AI systems.
Generative Adversarial Models for Privacy and Fairness
Explore generative adversarial models for enhancing privacy and fairness in machine learning, covering differential privacy, GANs, and real-world applications in data publishing and encoding.
The GDPR and Beyond - Ideas from Academia and Policy Panel
Exploring GDPR's impact on data protection, automated decision-making, and AI regulation. Experts discuss key concepts, implementation challenges, and future implications for governments and corporations.
The GDPR and Beyond - Elizabeth Denham, UK Information Commissioner
Elizabeth Denham discusses GDPR implementation, its impact on data protection, and future implications for AI regulation. Insights from the UK's top data protection official on privacy, innovation, and accountability.
Achieving Cooperative Outcomes in Mixed Settings
Explore strategies for achieving cooperative outcomes in diverse environments. Learn from Nisarg Shah's expertise in fairness, efficiency, and algorithmic decision-making affecting humans.
Unbiased Learning to Rank - Lessons from Baidu's Large-Scale Search Dataset
Explore real-world insights from Baidu's search data analysis, examining unbiased learning-to-rank methodologies and their practical implications for search engine optimization.
Introduction to Data Ethics - Brent Mittelstadt
Explore data ethics with Dr. Mittelstadt, covering algorithmic fairness, accountability, transparency, and privacy. Learn about ethical challenges in AI and data analytics, with real-world examples and regulatory insights.
Fairness in Machine Learning with Tulsee Doshi
Explore ML fairness principles, concerns, and mitigation strategies. Learn about Google's approach to inclusive AI development and tools for evaluating and improving fairness in machine learning models.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Explore cutting-edge data science topics with leading experts, including deep learning, reinforcement learning, robustness, fairness, and ethics, through keynote talks and panel discussions.
Ethics and Data: Where Does the Twain Meet?
Explore the intersection of ethics and data in NLP, ML, and AI research, addressing inclusivity, fairness, and transparency while navigating IRB, copyright, and licensing issues.
The Ethics of Breaking Barriers
Explore ethical barrier-breaking through a fifth grader's perspective. Learn to push limits fairly, respecting others and considering long-term impacts. Gain insights on speaking up, collaboration, and patience in pursuing progress.
Algorithmic Decision Making - Exploring Practical Approaches to Liability, Fairness, and Explainability
Explore practical approaches to liability, fairness, and explainability in algorithmic decision-making with industry experts.
Towards Causal Foundations of Safe AI - Tutorial
Explore causal foundations for safe AI, covering models, fairness, risks, and mitigation strategies to ensure ethical and beneficial AI decision-making.
Provably Fair and Robust Machine Learning
Explore cutting-edge methods for building fair and robust machine learning systems, covering tabular and vision data alongside deterministic and statistical certification approaches.
Don't Stop at Data Democratization - Data Literacy and Diversity in AI Systems
Discover how synthetic data drives innovation by enabling data diversity and equitable AI systems, while promoting inclusive access to data resources for all professionals.
Responsible AI 2.0 - The Imperative of Responsible AI Development
Explore the evolution of Responsible AI principles, focusing on fairness, accountability, privacy, and transparency in machine learning applications across various sectors of daily life.
Xavier Amatriain on Practical Deep Learning Systems - November 2019
Practical insights on deep learning systems, covering data handling, model selection, architecture engineering, and real-world implementation challenges in various domains.
Algorithmic Bias in Human Resources: Responsible AI and Business Strategy
Explore algorithmic bias in HR, responsible AI, and business strategy. Unpack root causes, identify symptoms, and develop strategies for responsible HR algorithms and AI projects.
How Can Machines Learn Human Values? - With Brian Christian
Exploring challenges in AI ethics, including data bias, fairness, and value alignment. Discusses innovative solutions for building more robust and ethically-aligned machine learning systems.
Inclusive Search and Recommendations
Explore inclusive AI in personalized discovery systems, focusing on fairness, diversity, and privacy. Learn strategies for reducing bias and implementing responsible AI in search and recommendation algorithms.
Approximately Stable Committee Selection
Explore committee selection methods, focusing on fairness, stability, and approximation algorithms. Learn about preference models, participatory budgeting, and innovative approaches to achieve balanced representation.