AI Explainability

What is AI Explainability?

Explainable artificial intelligence (XAI) refers to a set of processes and methods that allows humans to understand, comprehend and trust the results generated by machine learning algorithms.

XAI helps comprehend the ML model, its expected impact, and potential biases affecting performance level. Explainability fosters people’s trust and confidence in AI systems. It exemplifies model accuracy, transparency, and predictions generated by AI systems and constitutes a significant pillar of responsible AI.

XAI uses these two approaches to understand model behavior.

Global interpretation: The global approach provides a big-picture view of the model and how different features collectively produce a specific result.

Local interpretation: The local approach to AI explainability considers each instance in the data and individual features that affect model predictions.

AI explainability and AI interpretability are often used interchangeably. While interpretability refers to the degree to which users can comprehend the cause for a prediction, XAI refers to how users comprehend the output generated by the ML model. But the ultimate aim of interpretability and explainability is the same – understand the model.

Why does AI Explainability Matter?

AI systems lack a definitive approach of “if/then” logic to explain specific ML model predictions. This lack of transparency can result in poor decisions, distrust, and denial to use AI applications. AI explainability promotes better governance and bridges the gap between tech and non-tech teams to understand projects in a better way. It ensures the following benefits:

Auditability

XAI offers insights on various project failure modes, unknown vulnerabilities, and flaws to avoid similar mistakes in the future. It helps data science teams with better control of AI tools. 

Enhanced trust

AI serves high-risk domains and business-critical applications where trust in AI systems is not a choice but an obligation. XAI supports predictions generated by ML models with a strong evidence system.

Scaled performance

Deeper insights on model behavior help optimize and fine-tune the performance of ML models.

Compliance

Aligning with company policies, government regulations, citizen rights, and global industry standards is mandatory to businesses. Automated decision-making systems should comprehend their predictions with insights into the logic involved and possible consequences.  

Getting AI Explainability Right

A journey from AI to XAI is not easy but essential. XAI requires using appropriate techniques and tools to drive insights on model behavior and associated prediction logic. A few XAI tools and techniques include:

ELI5

It is a Python package that helps debug and visualize machine learning classifiers and explains their predictions. 

SHAP

SHapley Additive exPlanations is a game-theoretic approach helping explain the output generated by any ML model.

LIME

It stands for Local Interpretable Model Agnostic Explanation. It is a visualization technique that explains individual predictions and handles irregular input.

Other proven approaches to XAI include PDP, ICE, LOCO, ALE, and more. Image-specific explainability tools like Class Activation Maps (CAMs) and others like Integrated Gradients that apply to both text and images drive better explainability. Skater and AIX360 tools also support an open-source solution to ensure XAI.  

Further Reading

Explainability and Auditability in ML: Definitions, Techniques, and Tools