85 citations · 206 across the 12 of their papers we have counts for
13 papers
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?
Jakob Mokander, Prathm Juneja, David Watson +1
On the whole, the U.S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still…
The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems
Jakob Mokander, Margi Sheth, David Watson +1
Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap betwee…
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
David S. Watson, Joshua O'Hara, Niek Tax +2
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast,…
Intervention Generalization: A View from Factor Graph Models
Gecia Bravo-Hermsdorff, David S. Watson, Jialin Yu +2
One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapp…
Conditional Feature Importance for Mixed Data
Kristin Blesch, David S. Watson, Marvin N. Wright
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical pers…
Causal discovery under a confounder blanket
David S. Watson, Ricardo Silva
Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discove…