AI Explainability

AI explainability refers to the ability to clearly understand and interpret the internal decision-making process of an AI system. As machine learning models grow in complexity—particularly deep learning models—explaining why a model produced a certain output has become increasingly difficult. Yet in many business, legal, and safety-critical contexts, understanding those decisions is essential.

Explainability supports multiple goals:

• Trust: Users are more likely to adopt AI if they understand how it works.
• Accountability: Regulators and stakeholders need visibility into AI decisions that affect individuals or the public.
• Debugging: Developers must understand model behavior to improve performance and identify errors.
• Bias mitigation: Explainable models help detect and reduce unintended discriminatory outcomes.

There are two broad categories of explainability:

• Intrinsic: Simpler models (like decision trees) that are inherently easy to interpret.
• Post hoc: Tools or techniques applied after training to explain complex models, such as SHAP values or LIME (Local Interpretable Model-agnostic Explanations).

The need for explainability depends on the use case. A credit approval algorithm, for example, may require detailed explanations to satisfy compliance with fair lending laws. A product recommendation engine may require less scrutiny.

However, explainability has limits. Some deep learning models, especially large language models (LLMs), function as black boxes with decisions emerging from billions of parameters. Providing a faithful and useful explanation in such cases is difficult and sometimes misleading.

Explainability is not only about transparency—it’s about actionable transparency. Security and compliance teams must be able to validate whether a model is behaving within acceptable boundaries, especially in dynamic production environments.

How PointGuard AI Addresses This:
PointGuard AI brings runtime explainability to life by monitoring model outputs and correlating them with policy, data lineage, and risk scoring. The platform flags decisions that deviate from expected logic or regulatory rules and provides context-aware explanations tailored to technical and non-technical audiences. This helps organizations enforce AI governance, meet compliance requirements, and maintain user trust—even with complex or opaque models.

Resources:

EU GDPR

Carnegie Mellon: What is Explainable AI