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Application of the NIST AI Risk Management Framework to Surveillance Technology
This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology.
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology
This paper presents a theoretical analysis and practical approach to the moral responsibilities when developing AI systems for non-military applications that may nonetheless be used for conflict applications.
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Algorithmic harms are commonly categorized as either allocative or representational.
Constraint-Based Causal Structure Learning from Undersampled Graphs
Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data.
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning.
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.
Rate-Agnostic (Causal) Structure Learning
That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.
Tracking Time-varying Graphical Structure
Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary.
Integrating Locally Learned Causal Structures with Overlapping Variables
In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset.
