Researchers propose a method to quantify explainability in AI systems.
problem Lack of consensus and quantification of explainability in AI systems.
method Analyzed definitions from different disciplines, proposed a quantification approach.
result Proposed a reasonable and model-agnostic way to quantify explainability.
The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state. In addition, many systems, such as image classifiers, operate on low-level features rather than high-level concepts. To address these challenges, we introduce Concept Activation Vectors (CAVs), whic…
Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal embedding, where we want to learn an embedding in an Euclidean space of the given instances that preserves the comparison order as well as pos…
Survey on causal interpretability models for machine learning.
problem Understanding and explaining the decisions made by machine learning models.
method Comprehensive survey on causal interpretable models.
result Evaluation metrics for interpretability are discussed.
A new method improves adversarial robustness and interpretability with reduced training time.
problem Adversarial attacks on deep neural networks.
method A novel regularizer incorporating first and second order information via a quadratic approximation to the adversarial loss.
result Single iteration of the proposed regularizer achieves stronger robustness than prior methods.
DECE visualizes machine learning decisions with counterfactual explanations.
problem Making machine learning models transparent and explainable.
method Interactive visualization system supporting counterfactual explanations at instance- and subgroup-levels.
result DECE enables users to explore and understand machine learning model decisions.
Proposes a method for clearer counterfactual explanations of deep networks.
problem Unclear explanations from current counterfactual methods.
method Gradual construction of explanations through masking and composition steps.
result Produces human-friendly, interpretable explanations with fewer modifications.
Meta-learning framework improves explainability of GNNs.
problem Improving explainability of graph neural networks.
method Meta-learning framework to steer GNN training towards interpretable minima.
result Models are easier to explain by different algorithms without sacrificing accuracy.