Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey (2006.11371v2)

Abstract: Nowadays, deep neural networks are widely used in mission critical systems such as healthcare, self-driving vehicles, and military which have direct impact on human lives. However, the black-box nature of deep neural networks challenges its use in mission critical applications, raising ethical and judicial concerns inducing lack of trust. Explainable Artificial Intelligence (XAI) is a field of AI that promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions. In addition to providing a holistic view of the current XAI landscape in deep learning, this paper provides mathematical summaries of seminal work. We start by proposing a taxonomy and categorizing the XAI techniques based on their scope of explanations, methodology behind the algorithms, and explanation level or usage which helps build trustworthy, interpretable, and self-explanatory deep learning models. We then describe the main principles used in XAI research and present the historical timeline for landmark studies in XAI from 2007 to 2020. After explaining each category of algorithms and approaches in detail, we then evaluate the explanation maps generated by eight XAI algorithms on image data, discuss the limitations of this approach, and provide potential future directions to improve XAI evaluation.

Overview of Explainable Artificial Intelligence (XAI): A Survey

The paper by Das and Rad provides a comprehensive evaluation of the opportunities and challenges within the field of Explainable Artificial Intelligence (XAI). As AI systems are increasingly integrated into mission-critical applications, such as healthcare and autonomous vehicles, the black-box nature of deep learning models raises ethical and judicial concerns. This survey aims to address these concerns by dissecting the current landscape of XAI in deep learning.

Taxonomy of XAI Techniques

The paper proposes a taxonomy to categorize XAI techniques based on three dimensions: the scope of explanations, the methodology employed, and the level of integration with models.

1. Scope:
   • Local Explanations: These focus on interpreting individual predictions. Techniques such as LIME and SHAP are exemplars, which employ perturbation and game-theoretic approaches to attribute feature importance.
   • Global Explanations: These aim to provide insights into the model's behavior across the entire dataset. Global surrogate models and Concept Activation Vectors (CAVs) are highlighted for their ability to approximate complex models with interpretable ones.
2. Methodology:
   • Perturbation-Based: Explains model predictions by observing changes when input features are perturbed. Techniques include LIME, SHAP, and RISE.
   • Backpropagation-Based: Utilizes gradients for explanation, focusing on how input features impact model predictions through derivatives. Saliency maps and Grad-CAM fall in this category.
3. Usage Level:
   • Model Intrinsic: Include built-in explanations within the model's architecture. Generalized Additive Models (GAMs) demonstrate this approach.
   • Post-Hoc: Applied to trained models without modifying their structure. Methods such as DeepLift and LRP are post-hoc, often providing model-agnostic insights.

Evaluation of XAI Techniques

The paper discusses the significance of evaluating the effectiveness of XAI methods, emphasizing the necessity for explanations that are stable, consistent, and computionally feasible. Techniques like System Causability Scale (SCS) and Benchmarking Attribution Methods (BAM) are introduced to assess the quality and reliability of XAI outputs.

Implications and Future Directions

The survey underscores the critical role of XAI in fostering transparency, trust, and fairness within AI systems. It suggests the need for standardized benchmarks to evaluate XAI methods and highlights the potential for further research in developing robust, scalable solutions.

While the current state of XAI offers a promising toolkit, the authors highlight challenges, such as susceptibility to adversarial attacks and the limitations of visualization techniques. Building upon these insights, future work should focus on improving human interpretability and integrating ethical considerations into model design.

Conclusion

This survey by Das and Rad is a significant contribution to understanding the complex landscape of XAI. It not only categorizes existing methodologies but also sets the stage for future innovations in crafting interpretable and trustworthy AI systems. As AI's role in societal applications grows, the advancement of XAI will be integral to ensuring ethical and effective integration into various domains.