What is Explainability? - Shipshape Data

Explainability in AI refers to the ability to understand and interpret how an artificial intelligence or machine learning model arrives at its predictions or decisions. It answers the question: “Why did the AI make that choice?”

As AI becomes more deeply embedded in business and government decisions, explainability has become a core requirement for building trust, ensuring fairness, and meeting compliance standards.

Why Explainability Matters

Modern AI models, especially deep learning systems, often behave as “black boxes”, they produce outputs without showing how those results were generated. This lack of transparency can cause hesitation among stakeholders, regulators, and users.

Trust and accountability: Users and regulators must understand how decisions are made to ensure they’re fair and ethical.
Debugging and optimisation: Explainable systems help data scientists improve model accuracy and detect bias.
Regulatory compliance: Laws like GDPR require explainable decision-making in automated processes.
User adoption: Transparency increases acceptance of AI-driven tools in sensitive sectors like healthcare and finance.

Key Techniques in AI Explainability

There are two main categories of explainability methods, global and local. Global explainability explains how the model works overall, while local explainability explains individual predictions.

Feature Importance: Highlights which variables most influence a model’s output.
SHAP and LIME: Techniques that show how each input feature contributed to a specific prediction.
Partial Dependence Plots (PDPs): Visualise how changes in one variable affect outcomes.
Model Cards: Standardised documents that describe how and why a model was built, including limitations and bias considerations.

Explainability vs. Interpretability

These terms are closely related but not identical. Interpretability is the degree to which a model’s internal mechanics can be understood by humans. Explainability goes further, combining interpretability with communication, helping non-technical users understand results in plain language.

Challenges in Explainable AI

Complexity: Deep neural networks have millions of parameters, making them difficult to explain fully.
Trade-offs: Increasing transparency can sometimes reduce accuracy or performance.
Bias and trust: Even explainable systems can reproduce hidden bias if data quality is poor.
Standardisation: There’s no universal framework for measuring explainability yet.

Explainability and Ethical AI

Explainability is one of the pillars of Ethical AI. It ensures decisions are transparent, auditable, and justifiable. When users can see how models make decisions, it reduces the risk of bias, fosters accountability, and aligns AI systems with human values.

The Future of Explainable AI

As regulatory pressure grows and AI adoption increases, explainability will move from being a “nice to have” to a legal and ethical necessity. Companies that embrace it now will gain trust, resilience, and a competitive edge in the AI-driven economy.

In short, Explainability is about making AI human, transforming opaque algorithms into systems that can be understood, trusted, and improved.