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Learning From Strategic Agents: Accuracy, Improvement, and Causality
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' incentives to ``game'' their features in order to receive better decisions.
Frontier AI Regulation: Managing Emerging Risks to Public Safety
To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models.
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
As advanced machine learning systems' capabilities begin to play a significant role in geopolitics and societal order, it may become imperative that (1) governments be able to enforce rules on the development of advanced ML systems within their borders, and (2) countries be able to verify each other's compliance with potential future international agreements on advanced ML development.
Strengthening Subcommunities: Towards Sustainable Growth in AI Research
AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process.
Causal Strategic Linear Regression
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions.
Extracting Incentives from Black-Box Decisions
An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions.
