What is AI Explainability? Real-World Applications of Explainable AI

The black box problem in AI has been haunting developers and organizations for years. You deploy a sophisticated machine learning model, it makes predictions, but when stakeholders ask "why did it decide that?" – everyone is left shrugging. This is where AI explainability comes in, transforming opaque algorithms into transparent, interpretable systems that humans can actually understand and trust.

What is AI explainability?

AI explainability refers to the ability to understand and interpret how artificial intelligence systems make decisions. It's the difference between a model that spits out a recommendation and one that can articulate its reasoning process in human-understandable terms. Using the word ‘articulate’ loosely – because it won’t always (or even usually) be in the form of a natural language explanation. 

Foundations of AI explainability

At its core, explainable AI (XAI) addresses a fundamental challenge: as AI systems become more complex and powerful, they often become less interpretable. Deep neural networks with millions of parameters can achieve remarkable accuracy, but their decision-making process resembles a digital black box.

The foundation of AI explainability rests on several key principles:

• Transparency: The model's architecture and decision pathways should be comprehensible

• Interpretability: Outputs should be explainable in domain-relevant terms

• Accountability: Clear attribution of decisions to specific model components or data inputs

The Black box problem

The black box problem represents one of the most significant barriers to AI adoption in critical applications. Consider a medical diagnosis system that flags a patient for immediate intervention – without explainability, doctors can't understand the reasoning behind this recommendation. This lack of transparency creates several issues:

• Trust erosion: Stakeholders struggle to trust systems they can't understand

• Regulatory compliance: Many industries require transparent decision-making processes

• Debugging difficulties: Identifying and fixing model errors becomes nearly impossible

• Bias detection: Hidden biases in training data remain undetectable

AI explainability vs AI interpretability vs AI transparency

While these terms are often used interchangeably, they represent distinct concepts in the AI landscape:

• AI explainability focuses on providing post-hoc explanations for model decisions. It answers the "why" question after a prediction has been made.

• AI interpretability refers to the degree to which humans can understand the cause of a decision. It's often built into the model architecture itself.

• AI transparency encompasses the broader concept of making AI systems open and understandable, including data sources, training processes, and algorithmic choices.

Understanding these distinctions is crucial when securing AI-generated code and implementing robust AI governance frameworks.

Types of explanations in AI

Local vs. global explanations

Local explanations provide insights into individual predictions. For instance, when a loan application is rejected, a local explanation might highlight specific factors like credit score, income level, and debt-to-income ratio that influenced this particular decision.

Global explanations reveal overall model behavior patterns. They answer questions like "What factors does this model generally consider most important?" or "How does the model typically respond to different input combinations?"

Counterfactual explanations

Counterfactual explanations take a unique approach: "Your loan was denied, but if your credit score were 50 points higher and your income $10,000 more, it would have been approved." This type of explanation is particularly valuable because it provides actionable insights for users.

Example-based explanations

These explanations leverage similar historical cases to justify decisions. A medical diagnosis system might explain its recommendation by showing previous patients with similar symptoms and outcomes.

Feature-based explanations

Feature-based explanations break down decisions by attributing importance scores to different input variables. 

Rule-based explanations

Perhaps the most intuitive type, rule-based explanations express model logic as if-then statements: "If age > 65 AND previous heart condition = true, then recommend immediate consultation." Modern AI security tools, such as those leveraging Symbolic AI or SAST systems, often use this approach when analyzing AI-generated code for vulnerabilities.

Real-world applications of explainable AI in business

Technical approaches to AI explainability

Interpretable models

Some machine learning algorithms are inherently interpretable. Decision trees, linear regression, and rule-based systems naturally provide explanations for their decisions. While these models may sacrifice some accuracy compared to deep learning approaches, they offer transparency that's often valuable in business contexts.

Post-hoc explainability techniques

When using complex models like neural networks, post-hoc techniques can provide explanations after training:

• LIME (Local Interpretable Model-agnostic Explanations): Creates simple, interpretable models that approximate the behavior of complex models locally.

• SHAP (SHapley Additive exPlanations): Uses game theory to assign importance values to each feature.

• Attention mechanisms: In deep learning, attention weights can indicate which parts of the input the model focuses on.

Surrogate models

Surrogate models involve training a simpler, interpretable model to mimic the behavior of a complex black box model. While the surrogate may not capture every nuance of the original model, it provides a comprehensible approximation of the decision-making process.

Visualization techniques

Visual explanations can be particularly powerful, especially in computer vision applications. Techniques like saliency maps highlight which pixels in an image contributed most to a classification decision. Even outside of computer vision, dimensionality reduction methods like tSNE and PCA can produce visualizations that provide some intuition about the relationships among high-dimensional data.

Perturbation heatmaps, showing how small tweaks to an image (not noticeable to the human eye) can deceive AI models.

Model-specific explainability methods

Different types of models require different explanation approaches. For example, explaining a transformer's text generation involves analyzing attention patterns, while explaining a recommendation system might focus on user-item similarity matrices.

Practical implementation and governance

When implementing explainable AI systems, organizations must consider several practical factors:

• Performance trade-offs: More interpretable models often sacrifice some accuracy. Organizations need to balance explainability requirements with performance needs.

• Computational overhead: Generating explanations requires additional computational resources, which can impact system performance and costs.

• User experience: Explanations must be tailored to different audiences – technical teams need different information than end users.

Snyk's new AI Bill of Materials (AI BOM) tool provides valuable insights into these implementation challenges. Looking at real-world examples like the GroundingDINO project, we can see how complex AI systems integrate multiple models (BERT, ResNet variants) and libraries. The AI BOM visualization reveals dependencies that might impact explainability – for instance, understanding which components contribute to final predictions becomes crucial when multiple models work together.

Similarly, examining the OpenHands project through an AI BOM lens shows dependencies on various language models (Claude, GPT-4, Gemini), each with different explainability characteristics. This visibility helps teams make informed decisions about AI security risks and governance requirements.

AI governance and explainable AI

Evaluation of explainability in AI

Measuring explainability effectiveness requires both quantitative and qualitative approaches:

• Faithfulness: Do explanations accurately reflect the model's actual decision process? Reasoning Models Don’t Always Say What They Think, after all.

• Comprehensibility: Can target users understand and act on the explanations?

• Completeness: Do explanations cover all relevant factors influencing decisions?

• Actionability: Can users modify inputs based on explanations to achieve different outcomes?

Fairness, accountability, and transparency

Explainable AI plays a crucial role in ensuring fair and accountable AI systems. By making decision processes transparent, organizations can:

• Identify and address algorithmic bias

• Comply with regulatory requirements like GDPR's "right to explanation"

• Build stakeholder trust through transparent operations

• Enable meaningful human oversight of automated decisions

The intersection of explainability and AI security becomes particularly important when dealing with potential AI attacks or AI hallucinations. Understanding how models make decisions helps identify when they might be operating outside their intended parameters.

Challenges of explainable AI

Standardization and integration

The fragmentation of explainability approaches represents one of the most pressing challenges in the field. Organizations today face a bewildering array of explanation methods - LIME for model-agnostic interpretations, SHAP for feature attribution, attention mechanisms for transformers, and saliency maps for computer vision models. Each approach produces different types of explanations with varying formats, granularities, and underlying assumptions about what constitutes a "good" explanation.

This proliferation creates several critical problems. First, explanation shopping becomes possible - practitioners may unconsciously gravitate toward explanation methods that support their preconceptions rather than those that most accurately reflect model behavior. Second, comparing explanations across different models or even different runs of the same model becomes nearly impossible when using different explanation frameworks. Third, building robust explanation pipelines requires significant technical expertise to navigate the landscape of available tools and their respective trade-offs.

The lack of standardization extends beyond technical formats to fundamental philosophical questions about what explanations should accomplish. Should explanations focus on local decision boundaries, global model behavior, causal relationships, or counterfactual scenarios? Different stakeholders - data scientists, domain experts, regulators, and end users - often have conflicting requirements for explanations, leading to explanation frameworks that satisfy no one completely.

Moreover, the integration challenge compounds as organizations adopt multiple AI systems across different domains. A healthcare organization might use different models for medical imaging, drug discovery, and patient risk assessment, each requiring domain-specific explanation approaches. Without standardized explanation interfaces, maintaining consistent governance and oversight across these systems becomes a logistical nightmare.

The complexity increases when dealing with AI agents that can be subject to agent hijacking or other security vulnerabilities. Understanding the decision pathways of these systems becomes essential for maintaining a security posture.

Technical and practical limitations

Current explainability techniques face several limitations:

• Scalability issues: Generating explanations for large-scale models can be computationally expensive

• Explanation quality: Not all explanations are equally useful or accurate

• Context dependency: Explanations that work for one domain may not translate to others

• Dynamic environments: Models that adapt over time require evolving explanation strategies

Balancing accuracy and interpretability

Perhaps the most persistent challenge is the perceived trade-off between model accuracy and interpretability. While this trade-off isn't always inevitable, organizations often face pressure to choose between the most accurate model and the most explainable one.

Recent advances in explainable AI techniques are helping to bridge this gap. For instance, attention mechanisms in transformer models provide both high performance and interpretability. Similarly, techniques like neural additive models offer near-black-box performance with built-in interpretability.

Looking forward: The future of explainable AI

The field of explainable AI continues to evolve rapidly. Emerging trends include:

• Causal explanations: Moving beyond correlational explanations to causal understanding

• Interactive explanations: Allowing users to explore different explanation types and levels of detail

• Multi-modal explanations: Combining text, visual, and interactive elements for richer understanding

• Automated explanation generation: Using AI to generate better explanations of AI systems

As organizations increasingly adopt safe AI practices and implement comprehensive AI security frameworks, explainability becomes not just a nice-to-have feature but a fundamental requirement.

The integration of explainable AI with DevSecOps practices represents another frontier. As teams incorporate AI code review tools into their workflows, understanding how these tools make decisions becomes crucial for maintaining code quality and security.

Trustworthy and accountable AI

AI explainability represents a critical bridge between powerful AI capabilities and human understanding. As AI systems become more prevalent in critical applications – from healthcare to finance to autonomous systems – the ability to understand and trust these systems becomes paramount.

The challenges are real: balancing accuracy with interpretability, standardizing explanation formats, and scaling explainability techniques to enterprise levels. However, the benefits of explainable AI – increased trust, regulatory compliance, better debugging, and bias detection – make these challenges worth addressing.

Tools like Snyk's AI BOM provide valuable visibility into AI system components and dependencies, helping organizations understand the explainability landscape of their AI implementations. Whether you're dealing with security risks in AI coding or implementing secure AI-generated code practices, understanding your AI system's decision-making processes remains fundamental to success.

As we continue to push the boundaries of what AI can achieve, explainability ensures that humans remain in control, understanding not just what AI systems do but why they do it. This understanding forms the foundation for trustworthy, accountable, and ultimately more effective AI implementations across industries.

Ready to gain comprehensive visibility into your AI system's components and dependencies, and understand the explainability landscape of your AI implementations? Download our Deep Dive into AISPM today to learn more.

WHITEPAPER

What's lurking in your AI?

Explore AI Security Posture Management (AISPM) and proactively secure your AI stack.

Get the full guide