Kathleen Creel- Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems
Explore the ethical implications of algorithmic decision-making, focusing on arbitrariness at scale, its moral and legal issues, and potential technical solutions to mitigate harm.
Machine Learning and Social Norms - Balancing Fairness and Accuracy
Machine learning's impact on social decisions: Exploring fairness, privacy, and accountability in algorithmic decision-making across various domains. Discusses recent research and challenges in embedding social norms into predictive algorithms.
When Will We Need a Theory of Intelligence - Panel Discussion
Panel discussion exploring the need for a theory of intelligence, its potential components, and applications beyond human and machine intelligence, considering the limits of current AI successes and future challenges.
AI 判官，行不行？ - 探討人工智能在司法系統中的應用與挑戰
AI判官的可能性與挑戰：探討人工智能在法律領域的應用，以及對人權和司法公正的影響。人類如何在科技進步中保持自主權，塑造未來法律世界。
Besser natürlich dumm als künstlich intelligent - Über Generative KI und ihre Grenzen
Entdecke die Grenzen und Gefahren generativer KI: von halluzinierten Informationen bis zu militärischen Anwendungen. Erfahre, warum KI weder intelligent noch kreativ ist und welche Rolle sie in Zukunft spielen könnte.
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.
Be Prepared to Show Your Working! - Professor Sir David Spiegelhalter
Explore algorithmic decision-making transparency, validation, and explainability with Professor Spiegelhalter. Learn about the importance of demonstrating trustworthiness and intelligent transparency in automated advice systems.
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.
The Limits of Group Fairness and Predictive Multiplicity
Explore information-theoretic approaches to group fairness and predictive multiplicity in machine learning, examining their limitations and implications for trustworthy AI systems.
Algorithmic Decision Making and the Cost of Fairness
Explore algorithmic decision-making in pre-trial settings, examining bias identification, fairness concerns, and the trade-offs between accuracy and equity in automated systems.
Explainable AI for the Geosciences
Explore machine learning interpretation techniques in geoscience, focusing on climate science applications and their potential for scientific discovery and increased trust in AI outputs.
Sensible and Sensitive AI for Worker Wellbeing: Factors Informing Adoption and Resistance
Explores factors influencing adoption of AI systems for worker wellbeing, analyzing sensing methods, inference types, and data sharing preferences to guide worker-centered implementations.
Decoding AI - A Journey into Tomorrow's World
Explore AI's evolution, impact, and future possibilities. Unravel the mysteries of machine thinking, algorithmic dominance, and boundless innovation in this journey through tomorrow's technological frontier.
Community Keynote - Polinho Mota
Explore the intersection of technology, ethics, and society with insights from Polinho Mota in this thought-provoking keynote address at FAccT '24.
Fairness, Equality, and Power in Algorithmic Decision-Making
Explore the ethical implications of algorithmic decision-making, focusing on fairness, equality, and power dynamics in AI systems and their societal impact.
Toward a Theory of Race for Fairness in Machine Learning
Exploring critical race theory concepts for machine learning practitioners to develop a nuanced understanding of race in algorithmic fairness, through discussions and interactive activities.
Economic Models I
Exploration of fair machine learning through economic models, addressing equality of opportunity, population-level signaling, competitive equilibrium, and fair allocation algorithms.
Decoupled Classifiers for Group-Fair and Efficient Machine Learning
Explore group-fair machine learning with decoupled classifiers, balancing efficiency and fairness in algorithmic decision-making. Gain insights into innovative approaches for ethical AI development.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Explore the intersection of machine learning and mechanism design to address algorithmic fairness challenges in AI systems, focusing on innovative approaches and ethical considerations.
Can an Algorithmic System Be a 'Friend' to a Police Officer's Discretion?
Explore the intersection of algorithmic systems and police discretion, examining challenges, legal frameworks, and real-world applications in domestic abuse risk assessment and decision-making.
The Algorithmic Leviathan - Arbitrariness, Unfairness, and Opportunity in Algorithmic Decision Making
Explore the ethical implications of algorithmic decision-making systems, focusing on arbitrariness, unfairness, and potential solutions in automated processes across various domains.