Explainable Deep Learning: A Field Guide for the Uninitiated (2004.14545v2)

Abstract: Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input drive the decisions of a DNN. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN's decisions has thus blossomed into an active, broad area of research. A practitioner wanting to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field has taken. This complexity is further exacerbated by competing definitions of what it means to explain'' the actions of a DNN and to evaluate an approach'sability to explain''. This article offers a field guide to explore the space of explainable deep learning aimed at those uninitiated in the field. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning. We hope the guide is used as an easy-to-digest starting point for those just embarking on research in this field.

• The paper introduces core explainability methods—visualization, distillation, and intrinsic techniques—for interpreting deep neural network decisions.
• It categorizes visualization approaches into backpropagation- and perturbation-based methods, using tools like saliency maps and occlusion sensitivity to highlight feature impacts.
• It emphasizes user-centric evaluation and design, ensuring explanations align with diverse expertise levels in critical applications.

Overview of Explainable Deep Learning: A Field Guide for the Uninitiated

The paper "Explainable Deep Learning: A Field Guide for the Uninitiated" provides a comprehensive overview of the growing field of explainable deep learning (XDL). It aims to serve as a starting point for those unfamiliar with the domain, offering insights into various methods and dimensions that define the landscape of explainability in deep neural networks (DNNs). Given the increasing deployment of DNNs across critical domains such as healthcare, law enforcement, and finance, there is a pressing need for transparency and interpretability to ensure trustworthiness and reliability in these systems.

The paper introduces and categorizes explainability methods into three main dimensions: Visualization Methods, Model Distillation, and Intrinsic Methods. Each category encompasses a different approach to unravel the decision-making processes within DNNs, aiming to provide clarity on their operations to diverse end-users, from technical experts to non-specialists.

Visualization Methods

Visualization techniques focus on representing and understanding the influence of input features on model output. These methods are further divided into backpropagation-based and perturbation-based approaches. Backpropagation methods, such as saliency maps and class activation maps (CAM), explore the gradients and activations within the network to highlight relevant input areas. Perturbation-based methods, like occlusion sensitivity, involve altering input data to observe changes in model predictions, offering insights into feature importance.

Model Distillation

Model distillation aims to extract the knowledge embedded in complex DNNs into simpler, more interpretable forms. Two main strategies highlighted are local approximation and model translation. Local approximation methods, such as LIME, attempt to mimetically approximate the DNN's behavior within localized regions of the data manifold, using simpler models like linear classifiers. Model translation focuses on converting DNNs into more interpretable models, such as decision trees or rule-based systems, providing transparency at the aggregate level rather than merely local explanations.

Intrinsic Methods

Intrinsic methods involve modifying the architecture of the DNNs themselves to inherently incorporate an explanatory aspect. This includes using attention mechanisms, which allow the network to learn weighted importance over input features, providing clear insights into decision-making processes. Another approach within this category is joint training, where the primary task of the DNN is coupled with an auxiliary task specifically designed to render explanations, frequently taking a textual form.

Evaluation and User-Centric Design

The paper recognizes the challenges in evaluating the quality of explanations, pointing to objective measures such as fidelity, consistency, and comprehensibility in determining effective explanations. Human evaluations are also essential, as explanations should align with non-expert users' intuitive understanding, thereby enhancing the practical utility and adoption of XDL technologies.

User-centric design is another focal point, with emphasis on tailoring explanations to the expertise level and contextual needs of end-users. This highlights the necessity for user-friendly, efficient explanations, especially in decision-critical applications where timely and accurate interpretations are paramount.

Future Directions and Associations

Future research directions call for integrating explainability with robustness, trustworthiness, and fairness, given the ethical and societal implications of AI decisions. The paper also ties in related areas like model debugging, adversarial attacks, and fairness, indicating how these topics interact with explainability to fortify the reliability of DNNs.

In summary, the paper organizes the landscape of explainable deep learning methods and emphasizes the customization of these methods to cater to varied user needs and domains. By offering frameworks for evaluating and choosing appropriate methods, it acts as a guiding compendium for researchers and practitioners stepping into this critical field of AI.