Huang, Qiang, Yamada, Makoto, Tian, Yuan, Singh, Dinesh, Yin, Dawei, Chang, Yi

Graph structured data has wide applicability in various domains such as physics, chemistry, biology, computer vision, and social networks, to name a few. Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and generalization ability. GNN is a deep learning based method that learns a node representation by combining specific nodes and the structural/topological information of a graph. However, like other deep models, explaining the effectiveness of GNN models is a challenging task because of the complex nonlinear transformations made over the iterations. In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method. GraphLIME is a generic GNN-model explanation framework that learns a nonlinear interpretable model locally in the subgraph of the node being explained. More specifically, to explain a node, we generate a nonlinear interpretable model from its $N$-hop neighborhood and then compute the K most representative features as the explanations of its prediction using HSIC Lasso. Through experiments on two real-world datasets, the explanations of GraphLIME are found to be of extraordinary degree and more descriptive in comparison to the existing explanation methods.

Ignatiev, Alexey, Narodytska, Nina, Marques-Silva, Joao

Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used approaches for computing explanations are heuristic in nature. In contrast, recent work proposed rigorous approaches for computing explanations, which hold for a given ML model and prediction over the entire instance space. This paper extends earlier work to the case of boosted trees and assesses the quality of explanations obtained with state-of-the-art heuristic approaches. On most of the datasets considered, and for the vast majority of instances, the explanations obtained with heuristic approaches are shown to be inadequate when the entire instance space is (implicitly) considered.

Weld, Daniel S., Bansal, Gagan

Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and controlling otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This paper argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.

Madumal, Prashan, Miller, Tim, Sonenberg, Liz, Vetere, Frank

Causal explanations present an intuitive way to understand the course of events through causal chains, and are widely accepted in cognitive science as the prominent model humans use for explanation. Importantly, causal models can generate opportunity chains, which take the form of `A enables B and B causes C'. We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals---things that did not happen but could have under different conditions---they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. We employ a recurrent neural network to learn opportunity chains and make use of decision trees to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 RL benchmarks using different RL algorithms, and show that our model performs better in task prediction. We report on a study with 90 participants who receive explanations of RL agents behaviour in solving three scenarios: 1) Adversarial; 2) Search and rescue; and 3) Human-Agent collaborative scenarios. We investigate the participants' understanding of the agent through task prediction and their subjective satisfaction of the explanations and show that our distal explanation model results in improved outcomes over the three scenarios compared with two baseline explanation models.

The use of deep neural networks to make high risk decisions creates a need for global and local explanations so that users and experts have confidence in the modeling algorithms. We introduce a novel technique to find global and local explanations for time series data used in binary classification machine learning systems. We identify the most salient of the original features used by a black box model to distinguish between classes. The explanation can be made on categorical, continuous, and time series data and can be generalized to any binary classification model. The analysis is conducted on time series data to train a long short-term memory deep neural network and uses the time dependent structure of the underlying features in the explanation. The proposed technique attributes weights to features to explain an observations risk of belonging to a class as a multiplicative factor of a base hazard rate. We use a variation of the Cox Proportional Hazards regression, a Generalized Additive Model, to explain the effect of variables upon the probability of an in-class response for a score output from the black box model. The covariates incorporate time dependence structure in the features so the explanation is inclusive of the underlying time series data structure.