Interpretability vs. Explainability
Their key differences in AI and Machine Learning
In an era where artificial intelligence and machine learning are increasingly integrated into heavily regulated sectors such as healthcare, finance, and security, ensuring the transparency and trustworthiness of utilised models has become paramount. Users expect it, regulators increasingly demand it. Two concepts central to achieving this are interpretability and explainability. While the terms are often used interchangeably, they can be defined as offering distinct approaches to making AI decisions more comprehensible. Understanding these differences is essential for developers, regulators, and users alike.
AI and ML systems developed using deep-learning technologies are known as "black boxes,” producing decisions that are opaque to the average observer and difficult, if not impossible, for AI engineers to reverse engineer. This presents a challenge when AI is deployed in areas that directly affect human lives—such as making medical diagnoses or loan approvals—where trust in the system’s decisions is critical, where accuracy must be maintained, errors explained, and biases removed. To navigate this, stakeholders need to grasp why an AI made a certain decision. Enter interpretability and explainability—two pillars of transparency in AI.
While both interpretability and explainability aim to provide insight into AI’s inner workings, they differ in scope and application. Let’s delve into what sets them apart.
What is Interpretability?
Interpretability refers to how easily a human observer can understand why a machine learning model made a specific decision. In simple terms, a highly interpretable model allows one to map its inputs to its outputs clearly. Typically, interpretabile models tend to be simpler in their approach and domain of learning, using linear regressions or decision trees, where the mechanisms are more straightforward.
There are two interpretability sub-divisions of note; global and local interpretability. Global interpretability is used to refer model being wholly understandable, whereas local interpretability refers to the ability to trace specific individual decisions within a model.
What is Explainability?
In contrast to interpretability, explainability focuses on post hoc analysis of a model’s output—explaining why a model made a specific decision, even if the inner workings of a model remain opaque. This nuance makes explainability theoretically easier than interpretability in complex models like deep neural networks, where understanding the decision-making process is likely impossible due to the model’s intricate nature. Explainability tools often work by highlighting the key factors or model features that influenced a particular outcome.
Limitations of interpretability
While interpretability promotes transparency, the ability to interpret deep learning AI models often comes at the cost of accuracy. Simpler models might fail to capture the complexity and nuance required for high-performance outcomes, particularly in large datasets.
Limitations of explainability
The challenge with explainability is that it may oversimplify or misrepresent the actual processes at work in complex models. Generating these explanations also requires significant computational resources, which can slow down processes in real-time applications.
Key differences between interpretability and explainability
| Interpretability | Explainability | |
|---|---|---|
| Focus | concerned with understanding a model’s inner workings | focuses on explaining individual outcomes after the fact |
| Scope | applies to the entire model (global or local) | typically more localised, addressing specific inference outputs |
| Complexity | works best for simpler models | crucial for complex, black-box models, like deep learning algorithms |
| Benefits | Interpretable models are easier to debug and optimise due to their transparency | Explainable models can offer insights without needing to understand their inner mechanisms, aiding compliance with data and privacy regulations such as GDPR and AI regulation acts |
| Limitations | Interpretable models may sacrifice performance for simplicity | Explanations can mislead if they oversimplify a complex model’s behaviour |
While both interpretability and explainability aim to provide transparency in AI decision-making, they serve different purposes and are best suited for different types of models. Developers and businesses must choose the appropriate approach depending on the AI’s complexity, application, and the level of transparency required. Regulators, must of course, work to strike a balance between user needs and practicalities of artificial intelligence technologies. By leveraging interpretability and explainability techniques, developers and businesses alike can ensure that AI remains a trusted and reliable part of decision-making processes, particularly in domains where sensitive data is handled or where any data utilised in a training or decision-making process may lead to undesirable outcomes.
Interpretability and explainability within Literal Labs’ models
Literal Labs’ AI technology is unique. Developed to avoid the black box opaqueness of deep learning approaches, our models utilise a combination of binarisation, propositional logic, and Tsetlin machines. Despite deriving from a wholly different machine learning methodology, they continue to perform excellently. But it’s because the derive from a wholly different approach, that Literal Labs’ AI are wholly interpretabile and explainable—they're based on logic, so their training and inference can always be logically interpreted and explained.
If you'd like to understand how you can utilise our AI pipeline to build interpretabile and explainable models, please get in touch.