Beyond Black Boxes: Towards Explainable, Interpretable Causal AI (Part 1 – Terminology)

The new year will see parts of the EU AI Act coming into force as early as February 2025. There is a lot of uncertainty on the practical relevance of the core provisions and concepts of this Act. One major issue is the request for transparency. In this context terms like explainable and interpretable AI are often used. The objective of this edition on the Legal Informatics Newsletter is to explore what the meaning of these terms is and what relevance they might have in practise.

The following overview is of course comprehensive and further reading is highly recommended.

Introduction

Artificial Intelligence is a focus point of regulation in the EU. The European Union's AI Act emphasizes the importance of explainability, interpretability, and transparency in AI systems, particularly in high-risk applications. These principles are not only essential for ensuring compliance with regulatory frameworks but also for building trust among stakeholders and end-users. However, it remains unclear what is really making up the core of these terms.

This article aims to demystify the core terminology surrounding Explainable AI (XAI), providing a foundational understanding for readers. It will also explore how these concepts translate into technological implementation, addressing related challenges such as data privacy, continuous learning, and emerging approaches like Causal AI.

Key topics we will cover include:

• Definitions of explainability, interpretability, and transparency, and their distinctions.
• The trade-offs between black-box and white-box models in the context of high-stakes decisions.
• The role of Causal AI in providing deeper insights into decision-making processes.
• The relevance of AI Literacy in the context of explainable interpretable AI.
• Challenges related to data privacy and the transparency of continuously learning AI systems.

In particular for those who need to apply the EU AI Act in practise it will be key to get familiar with the terminology used in the context of transparent AI.

Key Terminology in Explainable and Interpretable AI

Understanding the core terminology is essential to grasp the challenges and opportunities associated with Explainable AI (XAI) and its implementation under regulatory frameworks like the EU AI Act.

Explainability

Explainability refers to the degree to which an AI system can provide understandable and meaningful insights into its operations and decision-making processes. It is the bridge between complex AI algorithms and human comprehension.

• Post-Hoc Explainability: Explanation generated after a model has been trained and deployed. For example: SHAP (Shapley Additive Explanations): Highlights the contribution of each feature to a model's prediction. LIME (Local Interpretable Model-agnostic Explanations): Provides approximate explanations for specific predictions.
• Inherent Explainability: Models that are inherently simple and interpretable, such as decision trees or linear regression.

Interpretability

Interpretability is the degree to which a human can understand how an AI model transforms inputs into outputs. While closely related to explainability, interpretability focuses more on understanding a model’s internal structure and mechanisms rather than explaining its outcomes.

• Examples of interpretable models: Linear Regression: Clear relationships between input features and predictions. Decision Trees: Easy-to-follow branching logic.

Key Distinction: Explainability often deals with making complex models understandable, while interpretability is inherent in simpler models.

Transparency

Transparency in AI refers to the openness of a system in revealing its architecture, design choices, and decision-making processes. It is a foundational requirement for building trust and ensuring compliance with regulations.

• Structural Transparency: How the system is built (e.g., algorithmic design, training data).
• Operational Transparency: How decisions are made and presented to users.

Transparency is critical for ensuring:

• Reproducibility: Verifying results with consistent methods.
• Audibility: Documenting processes for compliance and accountability.

Black-Box Models

Black-box models are AI systems, often powered by neural networks, whose internal workings are opaque or too complex to understand.

• Advantages: High accuracy in handling complex, unstructured data (e.g., images, natural language).
• Challenges: Lack of interpretability, raising concerns about trust and accountability. Difficulty in diagnosing errors or biases.

White-Box Models

White-box models are inherently interpretable and transparent, making them easier to audit and understand.

• Examples: Linear models, decision trees, rule-based systems.
• Advantages: Clear decision-making logic, ideal for high-stakes applications where explainability is a regulatory requirement.
• Limitations: Limited to a certain area of knowledge and rules. Lower accuracy compared to black-box models when handling complex patterns.

Causal AI

Causal AI focuses on understanding and modeling cause-and-effect relationships, going beyond the correlation-driven approach of most machine learning models.

• Key Benefits: Provides deeper insights by explaining why a decision was made rather than just how. Enhances transparency and interpretability by aligning with human reasoning.
• Applications: Healthcare: Identifying causal relationships in medical treatments. Public Administration: Understanding the impact of policy changes.

Causal AI is particularly relevant in the context of continuous learning and evolving AI systems, as it helps maintain consistent and meaningful explanations even as models adapt over time.

Broader Challenges in XAI

While the concepts of explainability, interpretability, and transparency are well-defined, implementing them in real-world systems poses several challenges. These challenges often arise from the inherent trade-offs between performance, complexity, and regulatory requirements. In this section, we explore some of the key issues that impact the deployment of Explainable AI (XAI) systems.

Data Privacy and Explainability

The interplay between data privacy and explainability is a significant concern, especially in high-stakes domains like healthcare, finance, and public administration.

• Balancing Privacy and Transparency: Many XAI techniques require access to detailed data to generate meaningful explanations, which can conflict with data protection laws such as GDPR. Privacy-preserving methods (e.g., differential privacy, federated learning) aim to protect sensitive information while enabling model interpretability. The request in the EU AI Act for keeping detailed log files is by the way an interesting topic in this context that will need much attention in the future as it might conflict with the request of privacy as also provided for widely in EU regulation.
• Challenges: Ensuring that explanations do not inadvertently expose sensitive or private information. Developing techniques that generate insights without compromising individual privacy.

Continuous Learning and Transparency

AI systems with continuous learning capabilities present unique challenges for transparency and accountability.

• What is Continuous Learning? Models that update dynamically based on new data, allowing them to adapt to changing environments or user behaviors.
• Challenges for Transparency: The model’s decision-making logic evolves over time, making it difficult to maintain a consistent explanation framework. Lack of visibility into what specific updates or data influenced a decision can hinder audits and accountability.
• Potential Solutions: Logging and versioning mechanisms to track changes in the model over time. Use of causal models to maintain consistent and interpretable explanations despite updates.

The Role of Causal AI in Addressing Challenges

Causal AI offers a promising approach to overcoming the challenges of traditional explainability methods, particularly in dynamic and privacy-sensitive environments.

• Why Causal AI Matters: Models cause-and-effect relationships rather than correlations, providing explanations that align with human reasoning. Offers stable and meaningful insights even as the system evolves through continuous learning.
• Advantages in XAI: Better alignment with regulatory requirements for transparency and accountability. Enhanced trust by providing explanations that are not only interpretable but also actionable.
• Challenges: Requires domain expertise to define causal relationships accurately. Computational complexity in large-scale systems. Privacy concerns remain.

Trust and Accountability

The ultimate goal of XAI is to build trust and accountability in AI systems. However, achieving this requires addressing several interrelated issues:

• Stakeholder Trust: Users and regulators must trust that AI systems are fair, unbiased, and reliable. Trust is eroded when explanations are overly complex, inconsistent, or fail to address user concerns.
• Accountability Mechanisms: Systems must enable users to trace decisions back to specific inputs or design choices. Organizations need governance frameworks to manage risks associated with explainability gaps.

AI Literacy

AI literacy is a key component for any practioner diving into the question of transparency, explainability and interpretability of AI systems as it is the ability to understand and critically evaluate AI systems, their outputs, and their limitations. In the context of XAI and the EU AI Act, getting to high levels of ?AI literacy is also crucial for ensuring that these systems are used effectively, responsibly, and ethically.

Designing Explainable AI Systems

Implementing explainability begins with making conscious design choices during the development of AI systems.

• Choosing the Right Model: Opt for inherently interpretable models (e.g., decision trees, linear regression) when transparency is critical. Use black-box models like neural networks only when the complexity and data demands justify their performance advantages.
• Integrating Explainability Tools: Use post-hoc explanation methods such as SHAP and LIME for black-box models. Combine visualizations (e.g., saliency maps) with textual explanations to make results accessible to non-technical stakeholders.

Balancing Accuracy and Interpretability

Achieving the right trade-off between model accuracy and interpretability is a central challenge in XAI.

·?????? Hybrid Approaches:

o?? Combine interpretable components with black-box models, such as using interpretable models for preliminary analysis and deep learning for more complex tasks.

·?????? Domain-Specific Customization:

o?? Tailor explainability solutions to the specific needs and expectations of the domain (e.g., healthcare vs. finance).

Impact of EU AI Act on Explainable and Interpretable AI

Transparency Requirements

The EU AI Act mandates transparency in AI systems, especially those classified as high-risk, to ensure safety, fairness, and accountability.

• Key Provisions for Transparency: AI systems must provide clear information about their purpose, capabilities, and limitations. High-risk systems must document decision-making processes, ensuring traceability. End-users must be informed when interacting with AI systems, particularly in automated decision-making contexts.
• Impact on Model Selection: Preference for white-box models in applications requiring high transparency, such as public administration or healthcare. Black-box models must incorporate robust post-hoc explainability techniques to meet transparency requirements.

Accountability and Oversight

The Act places a strong emphasis on accountability, requiring organizations to establish mechanisms for monitoring, auditing, and managing AI systems.

• Documentation and Record-Keeping: Organizations must maintain detailed records of training data, model architectures, and updates. Logs should allow regulators to audit decisions and trace outcomes back to their source.
• Auditability Challenges: Continuous learning models require additional measures to document evolving decision-making processes. Ensuring causal consistency in dynamically updated systems is critical for meeting regulatory standards.

Implications for Privacy, Continuous Learning, and Causal AI

The interplay between transparency requirements, data privacy regulations, and advanced AI methodologies presents unique challenges.

• Privacy Implications: Transparency efforts must comply with GDPR and other privacy laws, requiring innovative methods to explain decisions without exposing sensitive data. Differential privacy and synthetic data generation are becoming essential tools for balancing these requirements.
• Challenges with Continuous Learning: Evolving AI systems must provide consistent and meaningful explanations despite dynamic updates. Causal AI can help maintain interpretability by anchoring decisions in cause-and-effect relationships, even as systems learn from new data.
• Role of Causal AI in Oversight: Provides a robust framework for tracing and explaining decisions in complex and continuously learning systems. Enhances regulators' ability to assess compliance by offering clear insights into the causal drivers of AI outcomes.

Innovation Challenges:

High transparency and accountability requirements may increase development costs and complexity, potentially slowing innovation. In particular when the regulation itself is unclear and unspecific as it aims to regulate future development when the technology is quickly evolving.

Practical Implications for Developers, Policymakers, and Users

The principles and requirements outlined in the EU AI Act, along with the concepts of explainability, interpretability, and transparency, have far-reaching implications for various stakeholders. Here is a quick overview about some of the key topics:

Developers

For developers, the implementation of Explainable AI (XAI) is both a technical and strategic challenge.

• Integrating Explainability by Design: Incorporate inherently interpretable models where possible to reduce reliance on complex post-hoc methods. Use hybrid approaches that combine interpretable and high-performance models, ensuring a balance between accuracy and transparency.
• Leveraging XAI and Causal AI Tools: Adopt tools like SHAP, LIME, and DoWhy for providing meaningful explanations. Implement causal modeling techniques to enhance interpretability and traceability, especially in continuously learning systems.
• Continuous Learning Challenges: Establish robust logging and version control mechanisms to track changes and maintain accountability. Develop modular systems where components can be updated without disrupting explainability.

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Policymakers and Regulators

Policymakers (in particular in the EU member states) play a critical role in shaping the regulatory landscape to ensure ethical and effective AI deployment.

• Setting Standards for Explainability: Define clear guidelines for what constitutes acceptable levels of transparency and interpretability in high-risk applications. Encourage the adoption of causal AI and other advanced methodologies that align with the Act’s transparency requirements.
• Adapting to Continuous Learning Systems: Develop frameworks for monitoring and auditing systems that evolve dynamically, ensuring accountability without stifling innovation. Set clear requirements for organizations on how to document model outputs but also the rationale behind updates and learning processes.
• Balancing Privacy and Transparency: Provide clear guidelines on how to deploy AI applications and implement oversight mechanisms (like log files) in a way that does not conflict with privacy regulations

Example: Regulators overseeing autonomous vehicle systems could require causal explanations for decisions like sudden braking to ensure safety and trust.

6.3 End-Users

For end-users, transparency and explainability are critical to building trust and facilitating effective interactions with AI systems.

• Improving User Understanding: AI systems should provide explanations that are not only accurate but also accessible to diverse user groups. Simplify technical explanations into user-friendly formats, such as visual summaries or interactive interfaces.
• Empowering Users through AI Literacy: Support User AI Literacy Education Offer clear disclosures about system limitations and capabilities to manage expectations.

Collaborative Opportunities

The successful adoption of XAI and compliance with the EU AI Act require collaboration among developers, policymakers, and users.

Conclusion

Transparency of AI systems is clearly an important topic, not only on the regulatory side but also to build up trust in such applications. In order to empower those who need to apply the respective laws and regulations we need clear terminology and understanding of the underlying technical concepts.

We will dive deeper into this important topic in upcoming editions of the Legal Informatics Newsletter in 2025.