Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (1803.07517v2)

Abstract: Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods / interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.

• The paper introduces a taxonomy that classifies explanation methods for DNNs into rule-extraction, attribution, and intrinsic approaches.
• It highlights distinct user requirements by contrasting expert-driven and lay-friendly explanations to address transparency challenges.
• The paper discusses legal and ethical concerns, emphasizing GDPR's impact on fostering transparent and accountable AI systems.

Explanation Methods in Deep Learning: Users, Values, Concerns, and Challenges

The chapter "Explanation Methods in Deep Learning: Users, Values, Concerns, and Challenges", to be included in the upcoming Springer series on Explainable and Interpretable Models in Computer Vision and Machine Learning, offers a comprehensive analysis of the current landscape of explainable AI (XAI), primarily in the context of Deep Neural Networks (DNNs). The research explores the multifaceted components essential to XAI—users, laws and regulations, explanations, and algorithms—and evaluates the adequacy of explanation methods within these contexts. This essay aims to overview the paper's arguments and contributions, outline the significance of its findings, and anticipate future directions for this line of inquiry.

Components and Contexts of Explainability

The paper begins by contextualizing XAI within four main components: users, laws and regulations, explanations, and algorithms. This multi-dimensional framework provides a lens to scrutinize the current state of explainability pertinent to DNNs, which have become synonymous with 'black-box' systems. The authors highlight the necessity of trust in AI technologies as they permeate critical fields such as medical imaging, autonomous driving, and legislative processes. To illustrate, the paper cites real-world applications where DNNs have achieved superior performance, yet the opacity of their decision-making processes remains a major impediment to broader acceptance and trust.

User Categories and Concerns

A significant portion of the chapter focuses on identifying different user categories and their concerns. These are divided broadly into expert users and lay users, with further sub-categorizations within each. Expert users, such as DNN engineers and developers, require functional explanations for purposes such as model debugging and integration into complex systems. In contrast, lay users, including software application owners, end users, data subjects, and stakeholders, require a simplified understanding tailored to their non-expert backgrounds. The insights gathered from this analysis underscore the varying levels of transparency and interpretability required by different user types. Of particular interest is the speculation around explanation interfaces for lay users, an area notably lacking in the current XAI landscape.

Legal Considerations and the Impact of GDPR

The paper examines legal frameworks like the General Data Protection Regulation (GDPR), which mandates the provision of "meaningful information about the logic involved" in automated decision-making processes. This requirement spotlights the demand for DNN explanations that are comprehensible to lay users, thereby increasing accountability and societal acceptance. The challenges posed by legal obligations underline the need for robust explanation methods that bridge the comprehension gap between complex AI systems and everyday users.

Taxonomy of Explanation Methods

One of the key contributions of the chapter is the introduction of a taxonomy for explanation methods, categorized into rule-extraction methods, attribution methods, and intrinsic methods:

• Rule-Extraction Methods: These generate human-interpretable rules approximating a DNN's decision-making, with variations such as decompositional, pedagogical, and eclectic approaches, each having particular strengths in fidelity, interpretability, and generalizability.
• Attribution Methods: Focused on identifying important components within the input data, attribution methods often present results visually, aiding in intuitive understanding but occasionally lacking in reliability.
• Intrinsic Methods: Embedded within the DNN architecture itself, intrinsic methods aim to inherently increase the interpretability of DNNs by refining their internal representations.

Addressing General Concerns and Future Directions

The chapter articulates several general concerns related to data collection flaws, inscrutable data usage, and suboptimal inferences, among others. It acknowledges that while current explanation methods enhance interpretability and transparency, they do not wholly resolve issues like inherent biases and adversarial vulnerabilities. These findings suggest a pressing need for further interdisciplinary research, potentially involving insights from causality and formal verification domains, to build more resilient and unbiased AI systems.

Conclusion

In summary, this chapter makes significant strides in mapping out the landscape of explanation methods for DNNs, categorizing the existing methods, and articulating the diverse needs and concerns of users. It provides crucial insights into the challenges of designing explanation methods that balance transparency, interpretability, and legal compliance. This work, while setting the groundwork for future exploration into user-centric XAI solutions, also highlights areas ripe for innovation, particularly in developing intuitive explanation interfaces for non-expert users and addressing deeper ethical and legal concerns associated with AI deployment.