The terms “AI interpretability” and “explainability” (XAI) are frequently used but often misunderstood. This confusion is an expected part of grappling with a field that is itself in a state of rapid development and debate. This article aims to clarify the distinction between inherently interpretable AI—systems designed from the start to provide clear and direct causal paths from input to output—and explainable AI, which often starts as a ‘black box‘ requiring additional mechanisms for elucidation after decisions are made. We will explore the paradigms of AI interpretability and explainability, discuss situations where one approach may be favored over the other, and examine the benefits and challenges associated with each.

Overview of Inherently Interpretable AI

Inherently interpretable AI refers to systems explicitly designed to be transparent from their inception. These systems use straightforward algorithms that clearly illustrate the causal chain from input to output. This transparency ensures that the inner workings of the AI are accessible and easily comprehended by humans without needing additional explanatory frameworks.

Inherently interpretable AI systems commonly use algorithms designed for transparency, such as decision trees or linear regression. Each step and decision criterion in these models can be easily traced and understood. The objective is to make the decision paths so clear that anyone with reasonable technical knowledge can follow them without navigating complex layers or opaque structures.

Benefits of Inherently Interpretable AI 

The primary aim of inherently interpretable AI is to build systems that are straightforward and open by design, promoting:

  • Ease of Verification and Analysis: These models are constructed to be readily analyzed and verified. Their transparency ensures that each step of the AI’s decision-making is visible and comprehensible to developers, auditors, and end-users alike.
  • Immediate Trust and Acceptance: Because the workings of these models are clear and understandable, they tend to build trust quickly among users who can easily grasp how decisions are being made, fostering faster adoption in sensitive environments.
  • Simplicity and Reliability: Interpretable models typically involve simpler algorithms that avoid the complexities of deep learning or other black-box approaches, leading to more predictable and stable outputs.

When to Choose Inherently Interpretable AI

Opting for inherently interpretable AI is particularly advantageous in circumstances where:

  • Simpler Models Suffice: In cases where the tasks do not require handling vast amounts of data or incredibly complex patterns, simpler models can perform adequately without a black box’s computational overhead.
  • High-Stakes Environments Requiring Immediate Clarity: In fields such as healthcare diagnostics or air traffic control, where decisions need to be immediately interpretable to human operators without the need for further analysis or secondary validation.
  • Limited Scope for Error and Bias: In situations where the risk of bias or errors needs to be minimized, and the direct interpretability of the AI system allows for quicker detection and rectification of these issues.

Overview of Explainable AI (XAI)

Explainable AI (XAI) involves systems that, unlike inherently interpretable models, may initially function as “black boxes” where the decision-making process is not directly observable. XAI specifically addresses this by implementing methods to clarify and elucidate how these decisions are made after the fact. 

This approach contrasts with inherently interpretable AI, where the model’s inner workings are understandable by design, without additional layers of explanation. XAI’s purpose is to retrofit opacity with transparency, ensuring that users can understand, trust, and manage AI outputs, even when the underlying algorithms are complex and not straightforwardly clear.

Benefits of Explainable AI

Integrating XAI into complex AI systems offers unique advantages that differ significantly from those provided by inherently interpretable models:

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  • Retrofitting Transparency: Unlike inherently interpretable AI, which is transparent by design, XAI allows for the addition of transparency to pre-existing complex systems that were not originally designed with transparency in mind. This makes it possible to apply XAI to a broader range of existing technologies.
  • Detailed Post-Hoc Analysis: XAI provides detailed explanations for specific decisions after they are made, which is particularly useful for complex models where upfront transparency wouldn’t be a practical choice. Post-hoc elucidation can uncover insights into the model’s behavior that are not readily apparent, even with transparent model structures.
  • Adaptability to Advanced Models: XAI’s main purpose is to explain decisions from highly sophisticated models, such as deep neural networks, where traditional interpretable models may not suffice due to their simplicity and inability to handle certain complexities.
  • Enhanced Auditability: By making the outputs of black box models interpretable after the fact, XAI does a better job supporting audit trails than some interpretable models. This is crucial in regulated industries where understanding the ‘why’ behind a decision is as important as the decision itself.

When to Choose Explainable AI

Choosing XAI over inherently interpretable AI is particularly strategic in scenarios where:

  • The Complexity of AI Models Precludes Interpretability: In cases where the AI model must manage extremely complex data patterns that simpler, interpretable models cannot process effectively.
  • Post-Hoc Analysis Is Required: When stakeholders need to understand the basis of a decision after it has been made, especially for auditing or improving the model based on specific outcomes. 
  • Adaptability and Scalability: In situations requiring the scalability of AI systems without sacrificing the depth of insight into their functioning, particularly when these systems must evolve rapidly in response to new data or changing environments.
  • Balancing Performance with Transparency: In domains where the highest levels of AI performance are necessary but must also be accompanied by clarity on how decisions are made, such as in adaptive cybersecurity defenses or real-time financial trading algorithms.
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Built-in Transparency vs. Post-hoc Explanation—AI Interpretability vs Explainability

AI systems can be categorized based on how they deliver understandable outputs: Built-in Transparency, which is a feature of inherently interpretable AI, and Post-hoc Explanation, which is characteristic of explainable AI systems.

Built-in Transparency

Inherently interpretable AI is synonymous with built-in transparency. These systems are designed to be transparent from the outset, incorporating simplicity and clarity into their algorithms. This inherent transparency allows for real-time understanding of the AI’s decision-making process. 

The primary advantage is that the model’s logic is immediately understandable without requiring additional interpretation. Users can observe, in a straightforward manner, how inputs are processed into outputs, fostering immediate trust and facilitating easier regulatory compliance. This feature is particularly crucial for applications that require real-time decision verification, such as medical devices or automated trading systems.

Post-hoc Explanation

Explainable AI relies on post-hoc explanation methods. These AI systems, typically more complex and potentially more powerful, use advanced algorithms whose operations are not transparent from the outset. Instead, they require supplementary tools and techniques, such as LIME or SHAP, to unravel and explain how decisions were reached after the fact. This approach is necessary for AI models where the intricacy of the computations does not naturally lend itself to transparency, such as deep learning networks.

While post-hoc explanations can provide deep insights into the AI’s functioning, they also come with challenges. There’s often a trade-off between the accuracy and depth of the explanation and the complexity of the model. Additionally, these explanations can sometimes be approximate, not providing the full granularity of the decision process, or they might require considerable computational resources to generate, which can be a drawback in time-sensitive applications.

Model Complexity and Accessibility—AI Interpretability vs Explainability

The complexity of an AI model often determines its approach to explainability. This segmentation underscores a key divide: complex systems may require external explanatory mechanisms, whereas simpler models provide inherent transparency.

Explainable AI (XAI)

XAI is meant to elucidate the decision-making processes of complex models such as deep neural networks or ensemble methods that combine multiple learning algorithms to achieve greater predictive performance. These models involve layers of interconnected nodes and non-linear transformations that make their operations opaque. 

XAI approaches, such as Layer-wise Relevance Propagation (LRP) or Gradient-weighted Class Activation Mapping (Grad-CAM), are employed to backtrace the decisions of such networks, offering visualizations and summaries that explain which features most influenced the output and how. These techniques dissect the model’s complex architecture to highlight influential decision paths and feature interactions, offering a breakdown that renders the model’s sophisticated computations understandable.

Inherently Interpretable AI

Systems classified under inherently interpretable AI are built around simpler, more transparent algorithms that inherently facilitate human understanding. These models, like decision trees and linear regression, clearly display the relationship between input variables and the resulting outputs. 

The simplicity lies in their directness—each variable’s influence is visibly mapped out, showing a direct correlation that can be mathematically quantified. 

Application Areas and Suitability—Explainable vs Interpretable AI

The suitability of using either inherently interpretable or explainable AI models is dictated by the specific needs and constraints of the application environment. This consideration hinges on factors such as the requirement for real-time decisions, the complexity of data patterns, and the necessity for user trust and understanding.

Explainable AI (XAI)

XAI is particularly suitable for environments where the complexity of the data or the problem at hand exceeds the capabilities of simpler, interpretable models. These scenarios often involve high-dimensional data, more complex patterns, or situations where the stakes of decision-making are exceptionally high.

  • High-Dimensional Data: XAI thrives in settings where data features are numerous and their interactions complex. Techniques like SHAP or LRP allow stakeholders to dissect these interactions and gain insights into the significance and influence of each feature, even in a highly intricate model.
  • Complex Decision-making: In domains such as autonomous driving or complex system diagnostics, where decisions need to be made quickly and based on extensive data analysis, XAI provides a way to maintain high performance while still offering a route to understand the AI’s reasoning.

Inherently Interpretable AI

Inherently interpretable AI is best suited for applications where model simplicity is a virtue and decision-making transparency is paramount from the outset. These situations often require immediate clarity and ease of validation.

  • Simpler Data Patterns: When data patterns are straightforward or the decision-making process does not require capturing complex interactions, models like linear regression or decision trees can provide adequate performance along with complete transparency.
  • Real-time Decision-making: Environments that require immediate decision-making transparency, such as in clinical decision support systems where practitioners need to understand and trust AI recommendations quickly, benefit from the straightforward nature of interpretable models.
  • Educational and Training Purposes: In settings where the AI model is used as a tool for training or education, the directness of inherently interpretable models helps in demonstrating and teaching the fundamental principles of AI operations.
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User Trust and Understanding

Both approaches—AI interpretability and explainability— aim to foster user trust and understanding, but they achieve their goal in different ways. 

Inherently interpretable AI allows users to see directly into the decision-making process. This immediate transparency helps build trust quickly because users can easily grasp how decisions are being made. Explainable AI, on the other hand, cultivates trust gradually. Since the inner workings of the models are not immediately obvious, trust is built as users interact with the system and explore the explanations provided by post-hoc methods. Over time, as these explanations verify the AI’s decision-making processes and outcomes, users’ confidence in the system grows. 

Which Approach Is Best?

Interpretable AI offers inherent simplicity and clear decision-making processes, aligning well with regulatory frameworks that prioritize understandable and transparent AI operations. This directness not only simplifies compliance but also accelerates approval processes in regulated industries like healthcare and finance, where understanding the rationale behind each decision is crucial.

Explainable AI, while not immediately transparent, provides deep insights into the functioning of complex systems through post-hoc analysis. This ability to retrofit transparency into sophisticated models is invaluable, especially in scenarios utilizing advanced AI technologies where simplicity isn’t an option. However, achieving compliance with explainable AI may require additional efforts to comprehensively articulate and validate the logic behind the AI’s conclusions.

The debate between AI interpretability and explainability is ongoing. Both approaches are important, addressing different problems and functioning in various operational contexts. They ensure that AI’s integration into society and industries is both innovative and responsible, complying with existing laws and ethical standards.