22 citations · 128 across the 29 of their papers we have counts for
6 papers · 1 filter
A Psychological Theory of Explainability
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI…
Explainable AI for Natural Adversarial Images
Tomas Folke, ZhaoBin Li, Ravi B. Sojitra +2
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends o…
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be unde…
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi B. Sojitra +2
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts…
BayesDB: A probabilistic programming system for querying the probable implications of data
Vikash Mansinghka, Richard Tibbetts, Jay Baxter +2
Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? This paper describes BayesDB, a pr…
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
Vikash Mansinghka, Patrick Shafto, Eric Jonas +3
There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a…