Publications (22)
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Antti Hyttinen, Sergey Plis, Matti Järvisalo +2
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…
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation
Qiyue Gao, Xinyu Pi, Kevin Liu +21
Internal world models (WMs) enable agents to understand the world's state and predict transitions, serving as the basis for advanced deliberative reasoning. Recent large Vision-Lan…
Causal Graph Recovery in Neuroimaging through Answer Set Programming
Mohammadsajad Abavisani, Kseniya Solovyeva, David Danks +2
Learning graphical causal structures from time series data presents significant challenges, especially when the measurement frequency does not match the causal timescale of the sys…
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
Mohammadsajad Abavisani, David Danks, Sergey Plis
Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails…
Future of Pandemic Prevention and Response CCC Workshop Report
David Danks, Rada Mihalcea, Katie Siek +3
This report summarizes the discussions and conclusions of a 2-day multidisciplinary workshop that brought together researchers and practitioners in healthcare, computer science, an…
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Jennifer Chien, David Danks
Algorithmic harms are commonly categorized as either allocative or representational. This study specifically addresses the latter, focusing on an examination of current definitions…
Now More Than Ever, Foundational AI Research and Infrastructure Depends on the Federal Government
Michela Taufer, Rada Mihalcea, Matthew Turk +13
Leadership in the field of AI is vital for our nation's economy and security. Maintaining this leadership requires investments by the federal government. The federal investment in…
ION-C: Integration of Overlapping Networks via Constraints
Praveen Nair, Payal Bhandari, Mohammadsajad Abavisani +2
In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping…
Addressing the Unforeseen Harms of Technology CCC Whitepaper
Nadya Bliss, Kevin Butler, David Danks +2
Recent years have seen increased awareness of the potential significant impacts of computing technologies, both positive and negative. This whitepaper explores how to address possi…
Fairness Vs. Personalization: Towards Equity in Epistemic Utility
Jennifer Chien, David Danks
The applications of personalized recommender systems are rapidly expanding: encompassing social media, online shopping, search engine results, and more. These systems offer a more…
Dynamic Certification for Autonomous Systems
Georgios Bakirtzis, Steven Carr, David Danks +1
Autonomous systems are often deployed in complex sociotechnical environments, such as public roads, where they must behave safely and securely. Unlike many traditionally engineered…
Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI
Georgios Bakirtzis, Andrea Aler Tubella, Andreas Theodorou +2
Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contempo…
AI, Pluralism, and (Social) Compensation
Nandhini Swaminathan, David Danks
One strategy in response to pluralistic values in a user population is to personalize an AI system: if the AI can adapt to the specific values of each individual, then we can poten…
Linearity Properties of Bayes Nets with Binary Variables
David Danks, Clark Glymour
It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly…
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Mauricio Gonzalez Soto, David Danks, Hugo J. Escalante Balderas +1
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is cons…
Identification and Mitigating Bias in Quantum Machine Learning
Nandhini Swaminathan, David Danks
As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and ch…
Enabling the AI Revolution in Healthcare
Mona Singh, Katie Siek, David Danks +5
The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple syste…
Trustworthiness in Stochastic Systems: Towards Opening the Black Box
Jennifer Chien, David Danks
AI systems are increasingly tasked to complete responsibilities with decreasing oversight. This delegation requires users to accept certain risks, typically mitigated by perceived…
HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind
Nigel Doering, Rahath Malladi, Arshia Sangwan +2
Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We int…
Application of the NIST AI Risk Management Framework to Surveillance Technology
Nandhini Swaminathan, David Danks
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…
Homophily and Incentive Effects in Use of Algorithms
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta +2
As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. I…
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology
Daniel Trusilo, David Danks
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 use…