Executive summary:
Most AI systems function as "black boxes," with opaque decision-making processes. This lack of transparency poses significant risks, especially in sectors where failure could be damaging or even catastrophic.
AI explainability seeks to illuminate the decision-making processes of AI systems so that humans can better understand, steer, and govern them. Benefits of explainability include the following:
Explainable AI is an emerging set of architectures, practices, and techniques that help organizations understand and interpret AI decisions, practice good governance, and comply with regulations. Methods range from feature attribution techniques to visualization tools and modular AI architectures such as cognitive hive AI (CHAI).
Ready to explore how explainable AI can enhance your organization's AI capabilities? Contact Talbot West for a free consultation on implementing explainable AI tailored to your specific needs and industry requirements.
Artificial intelligence (AI) is becoming increasingly interwoven into government, business operations, and other critical systems. Most AI systems function as a black box, in which the decisions of the AI (and its processes to reach those decisions) are opaque.
AI has the potential to wreak havoc, especially in industries such as finance, health, or defense. On the milder end of the spectrum, it might decline a transaction, make a faulty diagnosis, or flag the wrong target. More catastrophic (and somewhat futuristic yet plausible) scenarios include shutting down power grids, launching weapons, crashing financial markets, and any number of other scary outcomes.
To circumvent AI-driven catastrophe and error, explainability seeks to illuminate the processes by which AI makes its decisions. With more transparency, humans can better steer AI away from bad outcomes and toward good outcomes.
Explainable AI is an emerging set of architectures, practices, and techniques that help organizations understand and interpret AI decisions, practice good governance, and comply with regulations.
AI models (particularly deep learning systems) take in data, process it through complex layers of mathematical operations, and produce outputs without providing insight into their decision-making process. Opacity exists due to the intricate nature of these models, which involve millions (or billions) of parameters and non-linear relationships that defy simple explanations.
The black box problem stems from the characteristics that make modern AI so powerful. Deep neural networks excel at finding subtle patterns in vast amounts of data, but their internal workings become obscure as they grow more sophisticated. Even the engineers who design these systems often can't explain why a particular input leads to a specific output.
This lack of transparency slows AI adoption and implementation for the following reasons:
The following methods attempt to make AI systems and machine learning models understandable. The goal of these techniques is to generate outputs that explain AI decisions to human users.
Feature attribution techniques identify which input features contributed most significantly to a particular prediction. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are two popular feature attribution methods.
SHAP assigns each feature an importance value for a particular prediction. It's based on game theory and provides consistent, locally accurate post-hoc explanations.
While SHAP can work with many models, it’s best for tree-based models. For deep learning models, it can be computationally expensive.
LIME creates a simpler, interpretable model that approximates the behavior of the complex model around a specific instance. This local model can then explain which features were most important for that particular model prediction.
LIME is model-agnostic, but it’s mostly used for explaining predictions of complex neural networks.
This is an extension of LIME that uses domain knowledge to create more realistic perturbations, leading to more accurate and relevant explanations.
LIME-SUP has niche applications that handle complex models in domains with well-understood feature relationships.
This approach finds similar instances in the training data to explain individual predictions. For instance, if an AI flags a transaction as fraudulent, it might show similar fraudulent transactions from the past to generate post-hoc explanations.
Primarily used on image classification models, Grad-CAM generates heat maps highlighting the regions of an image that were most important for the model's decision.
Grad-CAM is designed for convolutional neural networks and doesn't apply to other models.
PDPs show how a feature affects predictions on average, helping to understand the relationship between input features and the model's output.
PDPs work with many models but can be computationally intensive for large datasets or complex models.
For very complex models, a simpler decision tree can be trained to mimic the behavior of the black box model, providing an approximation of its decision process.
Decision trees are useful for approximating complex models, but the fidelity of the approximation fluctuates depending on the complexity of the original model.
These explain what changes would be necessary to alter the model's prediction. For example, "If a transaction was $50 instead of $100, it wouldn't have been flagged as suspicious."
Counterfactual explanations are model-agnostic but see heavy use in decision-making processes where understanding alternative outcomes is needed (e.g., loan approvals, fraud detection).
While these explainability techniques are quite technical, they are a necessary component of successfully implementing AI-based systems. And forward-thinking organizations aren’t shying away from illuminating opaque models to seize the full power of AI.
One exciting breakthrough in LLM explainability comes from the use of multiple LLMs (along with other types of AI or other forms of knowledge management) working together in a modular architecture. This approach, which we at Talbot West are calling cognitive hive AI (CHAI), offers a significant leap forward in our ability to understand and trust AI decision-making processes.
Here's how CHAI increases explainability:
Beyond enhanced explainability, CHAI has some other advantages over monolithic LLMs:
Let’s look at some specific AI implementations in which explainability plays an important role.
In finance, banks use machine learning models to assess loan applications. These models analyze hundreds of variables, from credit history to social media activity. Regulations such as the Equal Credit Opportunity Act require lenders to provide specific reasons for adverse actions. Here, SHAP values break down the model's decision.
A denied applicant might learn that their debt-to-income ratio contributed 40% to the negative decision, while their recent job change contributed 30%. This explanation satisfies regulatory requirements and helps applicants understand how to improve their chances in the future.
In healthcare, hospitals employ convolutional neural networks (CNNs) to detect anomalies in medical images such as X-rays or MRIs. To increase the transparency of CNNs, radiologists use Grad-CAM to generate heatmaps highlighting the areas of an image that most influenced the model's diagnosis.
In a chest X-ray flagged for potential pneumonia, Grad-CAM might highlight areas of lung opacity that the model focused on. This visual explanation allows radiologists to quickly validate the AI's findings and focus their attention on relevant areas. Example-based explanations also provide similar cases from the training data, helping less experienced radiologists understand the model's decision in the context of historical cases.
In manufacturing, factory operators use ensemble methods such as random forests to predict equipment failures based on sensor data. These models are challenging to interpret, making it difficult for maintenance teams to properly plan interventions. To solve this, manufacturers implement partial dependence plots (PDPs) to understand how different sensor readings impact failure predictions.
A PDP might reveal that vibration levels above a certain threshold dramatically increase failure probability. Computer scientists also apply SHAP values to help rank the importance of different sensors for each prediction. This allows maintenance teams to focus on the most critical indicators and understand the interplay between various factors. Explainable AI helps factories move beyond simple "failure imminent" warnings to actionable insights that guide precise, timely maintenance interventions.
E-commerce companies use large language models to power customer service chatbots. These models generate human-like responses to customer queries but sometimes produce inappropriate or incorrect answers. To mitigate this risk, companies implement a two-step explainable AI approach combined with human-in-the-loop features.
Human agents use post-hoc explanation methods such as LIME to identify which parts of the customer's input most influenced the chatbot's response. This helps debug unusual responses and improve the model's context understanding. Then, they employ counterfactual explanations to generate alternative responses based on slight changes in the input.
Human supervisors can quickly review and select the most appropriate response in ambiguous situations. Explainable AI lets companies maintain the efficiency of AI-driven customer service, exercise quality control, and build user trust.
Talbot West is here to guide you through every step of the implementation process. Here are the aspects we emphasize for our clients:
At Talbot West, we don't just implement RAG—we partner with you to create a solution tailored to your unique business needs. Our end-to-end support covers everything from initial setup to ongoing optimization.
Explainable AI (XAI) will help you get the most out of your organization's AI strategy. Here's how you can user confidence and accountability through explainable AI:
By making explainability a core part of your AI strategy, you'll build more trustworthy, effective, and valuable AI systems. The best way to successfully add explainability to your AI is to work with a partner that understands the process.
At Talbot West, we guide companies through the AI implementation process, including AI explainability. We help with:
Our mission is to blend your team's expertise and AI's capabilities, empowering your workforce and unleashing your organization’s potential.
Ready to explore XAI tools for your organization?
An example of explainable artificial intelligence is a credit risk assessment model for small business loans that uses a gradient-boosting algorithm.
The model provides a risk score and uses SHAP values to show how each factor (e.g., cash flow, credit history, time in business) contributes to the final score. It also offers what-if scenarios, showing how improving specific factors changes the risk assessment. This process helps loan officers and applicants understand the decision process and potential paths to approval.
Explainable artificial intelligence methods in natural language processing include:
Attention visualization, which highlights words or phrases the model focuses on when making predictions.
Layer-wise relevance propagation, which traces the contribution of each input word through the neural network layers.
Saliency maps identify which parts of a sentence most influenced the model's output.
For text classification tasks, LIME generates local explanations by altering the input text and observing changes in the model's predictions.
Monolithic “black box” LLMs are, by nature, not very explainable. Their explainability can be enhanced with some of the methods we discussed earlier, but there will always be a limit. At Talbot West, we believe that true explainability will come about through a modular, “hive” AI architecture that we’re calling “cognitive hive AI,” or CHAI.
CHAI spreads internal functions across modules, which allows a much higher level of transparency.
Explainable models are needed, especially in high-stakes domains such as healthcare, finance, and criminal justice. Explainable AI builds trust, enables regulatory compliance, and facilitates debugging and improving model performance. It helps detect and mitigate biases, enhancing fairness in AI-driven decisions.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
