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Ensuring AI systems are developed responsibly, with careful attention to fairness, transparency, and societal impact, building technology that benefits humanity while minimizing potential harms.

Overview

As AI systems become more powerful and widely deployed, ensuring they are safe, fair, and aligned with human values becomes increasingly critical. Our research in AI ethics and safety addresses both technical and societal challenges in creating responsible AI.

We work at the intersection of computer science, philosophy, policy, and social sciences to develop frameworks, tools, and methodologies for building AI systems that are trustworthy, accountable, and beneficial to society.

Current Research Focus

AI Alignment and Value Learning

How can we ensure AI systems pursue goals aligned with human values? We research techniques for learning human preferences, avoiding specification gaming, maintaining robustness to distributional shifts, and ensuring AI systems remain controllable as they become more capable.

Fairness and Bias Mitigation

AI systems can perpetuate or amplify societal biases. Our work includes developing fairness metrics and evaluation frameworks, detecting and mitigating bias in training data and models, ensuring equitable outcomes across demographic groups, and understanding the trade-offs between different fairness criteria.

Interpretability and Explainability

Understanding how AI systems make decisions is crucial for trust and accountability. We develop methods for explaining model predictions, visualizing learned representations, identifying influential training examples, and creating inherently interpretable architectures.

Robustness and Security

AI systems must be reliable and resistant to malicious attacks. Our research addresses adversarial robustness, distribution shift and out-of-distribution detection, privacy-preserving machine learning, and security against data poisoning and model extraction attacks.

Critical Research Areas

• AI Governance: Developing frameworks for responsible AI development, deployment, and oversight
• Impact Assessment: Evaluating potential societal impacts of AI systems before and after deployment
• Human Oversight: Designing effective human-in-the-loop systems for high-stakes decisions
• Privacy Protection: Ensuring AI systems respect individual privacy while maintaining utility
• Environmental Impact: Reducing the carbon footprint and energy consumption of AI training and deployment

Current Challenges

Key challenges include defining and measuring fairness across different contexts, balancing multiple competing ethical considerations, ensuring safety as AI systems become more autonomous, addressing the long-tail of edge cases and rare scenarios, and creating governance frameworks that keep pace with technological change.

Impact and Future Directions

AI ethics and safety research is increasingly recognized as fundamental to AI development rather than an afterthought. Organizations worldwide are establishing ethics boards, developing responsible AI principles, and investing in safety research.

Future priorities include developing more sophisticated alignment techniques for advanced AI, creating industry standards and regulatory frameworks, improving methods for auditing AI systems, advancing technical solutions for privacy and security, and fostering interdisciplinary collaboration between technologists, ethicists, policymakers, and affected communities.

Questions about our AI ethics and safety research? Get in touch