Key Takeaways:

• AI models are often opaque, making decisions without transparency.

• Explainable AI helps humans understand how and why AI makes decisions.

• It improves trust, adoption, productivity, and reduces business and regulatory risks.

• Highly regulated industries already rely on XAI to ensure fairness and accountability.

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More and more organizations are leveraging AI technology to enhance critical business functions.

However, the complex technology that makes AI so powerful also makes it hard to understand.  

Often, its decision-making is buried inside countless layers of calculations, making it difficult to determine how and why the AI generated a particular result.

When businesses base key decisions on AI results and trust it with customer interactions, it’s critical to fully understand the AI’s decision-making processes.

That’s where explainability comes in.  

Explainability helps developers confirm that the model is working as expected and may be necessary to meet regulatory standards in some industries. It also allows people affected by an AI’s decision to understand, challenge, or change that outcome.

Explainable AI helps people understand and explain machine learning (ML) algorithms, deep learning, and neural networks.

What is explainable AI?

Explainable AI is a set of practices that allows human users to understand and trust the output generated by machine learning algorithms.

Explainable AI helps define a model’s accuracy, fairness, transparency, and outcomes. For organizations that want to build confidence in an AI model they’ve put into production, explainability is critical. Additionally, it enables the business to adopt a responsible approach to AI development.

The Black Box Problem

AI technology is getting better every day. As it does, the people using it have to figure out how the algorithm arrived at a result.  

But the complexity of machine learning algorithms makes it nearly impossible. This can make an AI tool a “black box” that is impossible to interpret. Users see the input and output, but the reasoning that happens inside the algorithm is inscrutable.

Many times, even developers who built the algorithm can’t understand or explain precisely how the AI algorithm arrived at a specific result. The lack of explainability makes it hard to check responses for accuracy and leads to loss of control, accountability, and auditability.

That can sow distrust in the AI. If the algorithm suggests a change to your supply chain, for example, wouldn’t you want to know why? Would you trust it if the reasoning wasn’t clear?

Learn more: The Leader’s Guide to Measuring the ROI of AI Projects

How Explainable AI Works

With explainable AI, organizations can pry open the black box to access and adjust an AI tool’s underlying decision-making. It improves the user experience by fostering end-user trust in the AI’s decisions.  

Explainable AI uses specific methods to ensure that each decision a model makes can be tracked and understood. This transparency is essential for getting users to understand, trust, and effectively manage AI solutions.

Explainable AI involves three primary methods:

• Prediction accuracy

• Traceability

• Decision understanding

Prediction Accuracy

Accuracy is key to the success of an AI tool. Prediction accuracy can be determined by running simulations and comparing the explainable AI output to the results from the training dataset. Local Interpretable Model-Agnostic Explanations (LIME), which explains the prediction of classifiers by the ML algorithm, is currently the most popular method for determining prediction accuracy.

Traceability

Anyone operating in a regulated industry or environment knows the importance of traceability. To achieve traceability in AI, engineers limit the way decisions can be made and set up a tighter scope for ML rules and features. One example of a traceability technique in AI is DeepLIFT, which is short for Deep Learning Important FeaTures. DeepLIFT is a method for attributing the output of a neural network to its input features, providing insights into how the model arrived at its decision.

Decision Understanding

While prediction accuracy and traceability address technology needs, decision understanding addresses user needs—specifically, distrust. To use an AI solution efficiently, people must be educated about why it’s trustworthy. This involves clear, non-technical breakdowns of the AI engineering to provide users with an understanding of how and why the AI makes decisions.

Explainability vs. Interpretability in AI

Although they are similar, explainability and interpretability are not the same.  

Interpretability involves the user’s ability to understand how an AI made a decision. They may not see the step-by-step, but they nevertheless “get it.” It makes sense to them.

Explainability is exposing the step-by-step, or at least enough of it that users see how the AI arrived at the result.

How Explainable AI Relates to Responsible AI

Explainable AI and responsible AI have similar goals. However, where explainable AI focuses on an AI’s reasoning and output, responsible AI looks at the input and planning stages to ensure the AI is fed high-quality, non-biased data.

Explainable and responsible AI go hand in hand for improving the quality of an AI solution’s output.

Learn more: Is Investing in a Custom AI Solution Worth It?

Evaluating Models for Drift

It’s critical to regularly evaluate a model’s output for fairness, quality, and drift. As AI tools scale, they consume new data and modify the results they generate.  

Without governance and regular performance checks, AI output can gradually become biased and inaccurate, which exposes the organization to risk. Therefore, it’s essential to detect when a model drifts away from its normal functionality.

Regularly evaluating models also allows organizations to optimize model performance. Models may need retraining on new data to correct drift and eliminate any bias that has crept in.

Benefits of Explainable AI

Explainability builds trust, optimizes outcomes, mitigates risk, and improves regulatory compliance.

Fostering Trust

Removing the “black box” around an AI solution makes it less of a mystery and therefore fosters greater trust in the output. In turn, this fosters greater adoption and accelerates the journey from pilot to production. It also increases model transparency and traceability, which makes it easier to regularly evaluate models and prevent drift.

Accelerating Scalability

Explainable AI allows for systematic monitoring to continually evaluate and improve model performance. Teams that receive regular feedback on an AI tool’s performance can refine their development efforts and ensure the model steadily improves.

Mitigating Risk

Explainable, transparent AI models help maintain regulatory compliance, mitigate risk, and industry-specific requirements for data security and privacy. Explainability also minimizes costly errors and the risk of unintended bias.

Learn more: Why You Need a Chief AI Officer

Explainable AI Methods

Explainable AI methods are generally categorized by whether they explain the model globally (how it works overall) or locally (why it made a specific decision). Which method is most suitable largely depends on the use case and business requirements.

SHAP (SHapley Additive exPlanations)

SHAP is widely considered the gold standard for both local and global explanations. Based on cooperative game theory, it treats features as “players” and assigns them a “payout” (influence) based on their contribution to the prediction.

SHAP is mathematically consistent and robust. It provides a unified framework for both individual predictions and overall model behavior. It is best suited for industries where high accuracy is required, such as finance and healthcare.

LIME (Local Interpretable Model-agnostic Explanations)

LIME is the most popular model-agnostic tool. It works by slightly “perturbing” an input and seeing how the prediction changes. It then builds a simpler, interpretable model (like a linear regression) around that specific data point to explain it.

LIME is extremely fast and works on any model (text, images, or tabular). This makes LIME very good for quick prototyping and explaining unstructured data like text or images to non-technical stakeholders.

Integrated Gradients (IG)

Specifically designed for Deep Learning, IG attributes a model’s prediction to its input features by calculating the gradient of the output with respect to the input along a path.

IG is efficient for very large neural networks and satisfies “completeness” (the attributions sum up to the total prediction). It’s best suited to large-scale neural networks, computer vision, and NLP, where SHAP might be too slow.

Counterfactual Explanations

Instead of showing feature importance, this method answers the question, “What is the smallest change I could make to the input to get a different result?”  

This method is highly actionable for end-users and is particularly good for regulatory compliance, especially in relation to the GDPR’s “Right to Explanation.” It’s best suited for consumer-facing applications, such as credit scoring and insurance.

Saliency Maps / Grad-CAM

These are the most popular methods for Computer Vision. They generate heatmaps over images to show which pixels or regions the AI focused on when making a classification.

These methods are highly visual and intuitive, making them very effective for explaining image recognition and medical imaging (MRI/X-ray) analysis.

Learn more: AI: Cost Center or Revenue Driver?

The Role Explainability Plays

Explainable AI has become a fundamental requirement for any AI system operating at scale.  

As organizations mature from experimenting with AI pilots to scaling AI solutions across the enterprise, they’re finding explainability methods critical to establishing trust, mitigating risk, and maintaining regulatory compliance.

Businesses lacking the resources or bandwidth to implement trustworthy AI solutions at scale partner with Taazaa’s team of AI experts. We provide all the resources businesses need to succeed with AI—from strategy and design to development, integration, and support.  

Explore Taazaa’s AI services or contact us today to get started.  

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