Key Takeaways
- Interpretability and explainability aren’t the same: Interpretability helps you understand how a model works, while explainability helps you understand why it made a specific decision.
- Both concepts help make AI less of a black box: They give teams clearer visibility into model behavior and outputs.
- These approaches are increasingly important as AI is adopted in real-world settings: Contact centers, in particular, benefit from understanding how AI models support agents and customers.
- Interpretability goes deeper than explainability: Knowing the inner mechanics of a model provides a stronger foundation for trust, safety, and better decision-making.
In recent months, we’ve produced a tremendous amount of content about generative AI – from high-level primers on what large language models are and how they work, to discussions of how they’re transforming contact centers, to deep dives on the cutting edge of generative technologies.
This amounts to thousands of words, much of it describing how models like ChatGPT were trained by having them e.g. iteratively predict what the final sentence of a paragraph will be given the previous sentences.
But for all that, there’s still a tremendous amount of uncertainty about the inner workings of advanced machine-learning systems. Even the people who build them generally don’t understand how particular functions emerge or what a particular circuit is doing.
It would be more accurate to describe these systems as having been grown, like an inconceivably complex garden. And just as you might have questions if your tomatoes started spitting out math proofs, it’s natural to wonder why generative models are behaving in the way that they are.
These questions are only going to become more important as these technologies are further integrated into contact centers, schools, law firms, medical clinics, and the economy in general.
If we use machine learning to decide who gets a loan, who is likely to have committed a crime, or to have open-ended conversations with our customers, it really matters that we know how all this works.
The two big approaches to this task are explainability and interpretability.
Interpretability Defined
Interpretability is the ability to understand how an AI model processes information and arrives at a specific output. It focuses on revealing which inputs, features, or patterns most influenced the model’s decision. High interpretability helps users trust and validate the model’s behavior because it makes the decision-making process more transparent.
Explainability Defined
Explainability is the ability of an AI system to clearly communicate why it produced a certain result in a way humans can understand. It provides context, reasoning, or simplified representations of the model’s internal logic. Effective explainability bridges the gap between complex algorithms and user comprehension, making AI outputs more actionable and trustworthy.
Comparing Explainability and Interpretability
Broadly, explainability means analyzing the behavior of a model to understand why a given course of action was taken. If you want to know why data point “a” was sorted into one category while data point “b” was sorted into another, you’d probably turn to one of the explainability techniques described below.
Interpretability means making features of a model, such as its weights or coefficients, comprehensible to humans. Linear regression models, for example, calculate sums of weighted input features, and interpretability would help you understand what exactly that means.
Here’s an analogy that might help: you probably know at least a little about how a train works. Understanding that it needs fuel to move, has to have tracks constructed a certain way to avoid crashing, and needs brakes in order to stop would all contribute to the interpretability of the train system.
But knowing which kind of fuel it requires and for what reason, why the tracks must be made out of a certain kind of material, and how exactly pulling a brake switch actually gets the train to stop are all facets of the explainability of the train system.
Explainability in Machine Learning
Before we turn to the techniques utilized in machine learning explainability, let’s talk at a philosophical level about the different types of explanations you might be looking for.
Different Types of Explanations
There are many approaches you might take to explain an opaque machine-learning model. Here are a few:
- Explanations by text: One of the simplest ways of explaining a model is by reasoning about it with natural language. The better sorts of natural-language explanations will, of course, draw on some of the explainability techniques described below. You can also try to talk about a system logically, by i.e. describing it as calculating logical AND, OR, and NOT operations.
- Explanations by visualization: For many kinds of models, visualization will help tremendously in increasing explainability. Support vector machines, for example, use a decision boundary to sort data points and this boundary can sometimes be visualized. For extremely complex datasets this may not be appropriate, but it’s usually worth at least trying.
- Local explanations: There are whole classes of explanation techniques, like LIME, that operate by illustrating how a black-box model works in some particular region. In other words, rather than trying to parse the whole structure of a neural network, we zoom in on one part of it and say “This is what it’s doing right here.”
Approaches to Explainability in Machine Learning
Now that we’ve discussed the varieties of explanation, let’s get into the nitty-gritty of how explainability in machine learning works. There are a number of different explainability techniques, but we’re going to focus on two of the biggest: SHAP and LIME.
Shapley Additive Explanations (SHAP) are derived from game theory and are a commonly-used way of making models more explainable. The basic idea is that you’re trying to parcel out “credit” for the model’s outputs among its input features. In game theory, potential players can choose to enter a game, or not, and this is the first idea that is ported over to SHAP.
SHAP “values” are generally calculated by looking at how a model’s output changes based on different combinations of features. If a model has, say, 10 input features, you could look at the output of four of them, then see how that changes when you add a fifth.
By running this procedure for many different feature sets, you can understand how any given feature contributes to the model’s overall predictions.
Local Interpretable Model-Agnostic Explanation (LIME) is based on the idea that our best bet in understanding a complex model is to first narrow our focus to one part of it, then study a simpler model that captures its local behavior.
Example of Explainability in Machine Learning
Let’s work through an example. Imagine that you’ve taken an enormous amount of housing data and fit a complex random forest model that’s able to predict the price of a house based on features like how old it is, how close it is to neighbors, etc.
LIME lets you figure out what the random forest is doing in a particular region, so you’d start by selecting one row of the data frame, which would contain both the input features for a house and its price. Then, you would “perturb” this sample, which means that for each of its features and its price, you’d sample from a distribution around that data point to create a new, perturbed dataset.
You would feed this perturbed dataset into your random forest model and get a new set of perturbed predictions. On this complete dataset, you’d then train a simple model, like a linear regression.
Linear regression is almost never as flexible and powerful as a random forest, but it does have one advantage: it comes with a bunch of coefficients that are fairly easy to interpret.
This LIME approach won’t tell you what the model is doing everywhere, but it will give you an idea of how the model is behaving in one particular place. If you do a few LIME runs, you can form a picture of how the model is functioning overall.
Benefits of Explainability
Explainability brings several key advantages that strengthen both model performance and stakeholder trust:
- Builds Confidence and Transparency: By revealing why a model made a certain prediction, explainability reduces the “black box” effect and helps users feel more comfortable relying on AI-driven decisions.
- Improves Error and Bias Detection: Clear insights into model reasoning make it easier to spot inaccuracies, unintended patterns, or biased outcomes before they create real-world issues.
- Supports Accountability in High-Stakes Use Cases: Industries like healthcare, finance, and employment require explainable decisions to ensure fairness, compliance, and ethical use of AI.
- Speeds Up Debugging and Optimization: Engineers can more efficiently identify which features drive model behavior, enabling faster iteration and more targeted improvements.
- Enhances Communication With Non-Technical Stakeholders: Explainability simplifies complex model logic so business leaders can validate results, make informed decisions, and better integrate AI into workflows.
Together, these benefits make explainability a crucial component of deploying machine learning systems that are trustworthy, safe, and effective.
Interpretability in Machine Learning
In machine learning, interpretability refers to a set of approaches that shed light on a model’s internal workings.
SHAP, LIME, and other explainability techniques can also be used for interpretability work. Rather than go over territory we’ve already covered, we’re going to spend this section focusing on an exciting new field of interpretability, called “mechanistic” interpretability.
Mechanistic Interpretability: A New Frontier
Mechanistic interpretability is defined as “the study of reverse-engineering neural networks”. Rather than examining subsets of input features to see how they impact a model’s output (as we do with SHAP) or training a more interpretable local model (as we do with LIME), mechanistic interpretability involves going directly for the goal of understanding what a trained neural network is really, truly doing.
It’s a very young field that so far has only tackled networks like GPT-2 – no one has yet figured out how GPT-4 functions – but already its results are remarkable. It will allow us to discover the actual algorithms being learned by large language models, which will give us a way to check them for bias and deceit, understand what they’re really capable of, and how to make them even better.
Benefits of Interpretability
Interpretability offers essential advantages by making it clearer how a model processes inputs and arrives at its outputs:
- Increases Transparency Into Model Behavior: Interpretability helps teams understand which features or data points influence predictions, reducing uncertainty around how the model “thinks.”
- Improves Debugging and Quality Control: When engineers can trace decision paths, they can more easily diagnose performance issues, identify data problems, and refine the model’s structure.
- Supports Fairness and Bias Mitigation: By revealing which factors drive decisions, interpretability makes it easier to spot and correct biased patterns early in the modeling process.
- Strengthens Stakeholder Trust: Clear visibility into model logic reassures users, especially in regulated industries, that the system behaves logically and consistently.
- Enables Better Model Selection: Interpretability allows teams to compare models not just on accuracy, but on how understandable and predictable their decision-making is, leading to more reliable deployment choices.
Overall, interpretability helps ensure machine learning models are not only high-performing but also transparent, responsible, and easier to validate in real-world settings.
Why are Interpretability and Explainability Important?
Interpretability and explainability are both very important areas of ongoing research. Not so long ago (less than twenty years), neural networks were interesting systems that weren’t able to do a whole lot.
Today, they are feeding us recommendations for news, entertainment, driving cars, trading stocks, generating reams of content, and making decisions that affect people’s lives, forever.
This technology is having a huge and growing impact, and it’s no longer enough for us to have a fuzzy, high-level idea of what they’re doing.
We now know that they work, and with techniques like SHAP, LIME, mechanistic interpretability, etc., we can start to figure out why they work.
Final Thoughts on Interpretability vs. Explainability
Large language models are reshaping how contact centers operate, delivering new levels of efficiency and customer satisfaction. Yet despite their impact, much of what happens inside these models remains difficult to fully understand.While no contact center manager needs to become an expert in interpretability or explainability, understanding these general concepts can help you make smarter, safer decisions about how to adopt generative AI.
And if you’re ready to explore those possibilities, consider partnering with one of the most trusted names in agentic AI. Quiq’s platform now includes powerful tools designed to make agents more efficient and customers more satisfied. Set up a demo today to see how we can help you elevate your contact center.
Frequently Asked Questions (FAQs)
What’s the difference between interpretability and explainability?
Interpretability shows you how a model works, what features it uses, and how it processes information. Explainability shows you why the model made a specific decision, giving you a clear, human-friendly rationale for an output. Together, they help demystify AI behavior.
Why are these concepts important?
They provide visibility into systems that would otherwise operate as black boxes. This transparency helps teams trust model outputs, validate that the system behaves as expected, and ensure AI aligns with business goals and ethical standards.
Can a model be explainable without being fully interpretable?
Yes. Complex models like large language models may not reveal every internal mechanism, but they can still provide useful explanations for their predictions. This allows teams to work confidently with high-performing models without needing full access to their internal logic.
How do interpretability and explainability support better decision-making?
They help teams pinpoint why an output occurred, identify potential issues like bias or data drift, and troubleshoot unexpected behavior. This leads to safer, more reliable AI deployments and faster iteration on model improvements.
Do contact centers need deep expertise in these areas?
Not at all. Leaders simply need enough understanding to ask the right questions and evaluate whether an AI tool behaves consistently, safely, and in line with customer experience goals. A vendor like Quiq helps handle the heavy lifting.
