‍TL:DR

AI interpretability refers to how much humans can understand an AI system. Often, the complex algorithms that make AI models possible are shrouded in mystery, but strong interpretability makes it easier to establish trust among consumers while ensuring that businesses adhere to ethical AI practices. To do this effectively, businesses can incorporate interpretability into their models from the beginning of development, provide transparency to stakeholders, and monitor their models even after deployment.

Introduction

AI tools might be the "way of the future," but continuous anonymity and increasing data privacy invasions have users questioning its efficacy. A recent study found that companies in every sector of the economy have adopted the technology, but its use in production is uneven, with clustered usage in large companies and industries like healthcare.

The issue with AI adoption in sensitive industries like healthcare, however, is consumer concern over personal data.

Up to 67 per cent of participants in a PEW Research study have said they have little to no understanding of what companies are doing with their personal data. Most even believe they have little to no control over what companies or the government do with their data.

That's where interpretability comes to the rescue. Transparency is crucial here, and AI interpretability can help companies gain trust with it.

What Is Interpretability in Machine Learning?

Interpretability in AI is how deeply a model's internal processes and decisions can be understood and explained. For high interpretability levels, a business must have high model transparency and a deep understanding of how and why it's generating decisions and predictions.

When people can trust your product, it's easier for them to want to use it. Moreover, it helps set industry standards when it comes to ethical AI use — a crucial component in this emerging market.

Key Takeaways

• Differentiating between interpretability and explainability is key to helping consumers develop a holistic understanding of models.
• Utilizing techniques like model-agnostic methods, intrinsically interpretable models, and visualization tools can help increase interpretability.
• Integrating interpretability from the beginning, tailoring explanations to stakeholders, and continuous monitoring and improvement are essential best practices for transparency.

Understanding AI Interpretability

AI interpretability involves how easily a system’s internal processes and decisions can be understood and then explained by humans. It’s necessary in both the development and deployment processes because it helps ensure they’re being used ethically, and that they’re trustworthy among key stakeholders.

Interpretability vs Explainability

AI interpretability helps consumers understand the inner workings of a model, while explainability focuses on explaining each decision that's made. In some ways, these two words can be used interchangeably, but it's important that you're able to differentiate between the two when building models.

In most situations, high interpretability can come at the cost of overall performance. In the case of models that achieve high performance within companies that have a general understanding of the model interpretability, explainability begins to play a larger role.

Types of Interpretability:

There are two main types of AI interpretability.

Intrinsic Interpretability

Intrinsic interpretability comes with models that are inherently interpretable. These models work to reduce the complexity of the machine learning model by utilizing simple structures, like short decision trees. This keeps the relationships between input variables and predictions intuitive for consumers.

The caveat with intrinsically interpretable models, however, is that they often come with lower performance capabilities.

Post Hoc Interpretability

Post hoc interpretability applies interpretation methods after model training takes place.  It can be applied to simpler models alongside more complex ones, and it can be performed at both a local level and a global level.

Importance of AI Interpretability

There are three main areas AI interpretability is most useful.

Building Trust

Fostering trust and confidence in AI systems by enhancing transparency and providing users with the ability to judge AI decisions is imperative. Transparent data observability is the ultimate tool you can hand over to users, and it's perfect for building credibility.

Ensuring Accountability

Transparent AI auditing practices are an essential part of data protection. The more your business strives for consistency, compliance, and continuous improvement, the easier it is for customers to trust not only an AI model but also your business's data security.

Facilitating Regulatory Compliance

By facilitating regulatory compliance, you verify that your AI systems adhere to relevant laws and standards. However, with compliance comes action. It's just as important to set up processes that allow you to act on audit findings when necessary. There's no use monitoring if you can't react to what you find.

Techniques for Enhancing AI Interpretability

Model-Agnostic Methods

• Local Interpretable Model-Agnostic Explanations (LIME): The LIME approximates any black box machine learning model with a local model to explain each prediction.
• SHapley Additive exPlanations (SHAP): The SHAP framework is based on Shapley values, and it uses game theory to assign importance values to input features. This helps quantify their contributions to the model's output.

Intrinsically Interpretable Models

Intrinsically interpretable models have interpretability built-in, so they are easier to understand. They're naturally interpretable because they follow a hierarchical structure:

• Decision trees: Utilize a tree structure to map out the possible solutions to each decision made. This makes following the logic much easier.
• Linear regression models: Express the relationship between features and outputs as a linear equation, making it easier for users to understand how each feature affects the outcome.
• Rule-based systems: Applies man-made rules to sort, manipulate, and store data. That predictability is then used to make decisions.

Visualization Tools

Visualization tools are great for providing users with a clear definition of the logic and pathways that an algorithm utilizes to make decisions. When you use examples like decision trees, it's easier for stakeholders to gain insights into the model and potential trends.

A feature-important analysis is great for this. It involves calculating a score for all the features in a model to establish each one's importance. The higher the score, the more it affects the model. This type of analysis is effective because it allows consumers to understand the relationship between the features and the decisions they make.

It also helps developers build the model since they'll gain a better understanding of what features are irrelevant to the model.

Visualization can take a lot of the guesswork out of AI interpretability, as it gives users concrete data about what's relevant. You have a clear-cut picture of what customers value and what can be eliminated from the model altogether. It helps ensure that needs are being met throughout the model.

Best Practices for AI Interpretability

There are three main best practices you can utilize to help provide successful AI interpretability.

Integrate Interpretability from the Start

From the very beginning, think like your customer. In doing so, it's simpler to infuse interpretability into the model, and it becomes easier to build something understandable and intuitive.

Tailor Explanations to Stakeholders

To best tailor explanations to stakeholders, there are a few basic steps to take:

• Cut out the tech jargon.
• Know how to translate to both technical and non-technical users.
• Focus on results and output.

Whether you're talking to a data scientist or a line-level worker, they're both going to be interested in what the model does. Having explanations tailored to both parties is essential.

Continuous Monitoring and Improvement

Interpretability doesn't stop after deployment. Work to create a space for stakeholders to leave feedback and work to create consistent communication with them. Acknowledging feedback and providing regular updates are two great ways to do this. Consistent and ongoing assessment is essential, and so is incorporating stakeholder feedback in the refinement of interpretability methods.

Challenges in AI Interpretability

While you can work to maintain data protection from the founding of model building, nothing comes without risks.

For example, AI's use in oncology is essential for the automation of complex tasks, with everything from consent management to providing insights into patient care. However, with the building complexities of these models comes the sacrifice of interpretability — it's essential to keep consumer trust when this happens.

The priority in these cases should be limiting potential or actual bias through compliance. The easiest way for developers to overcome this challenge is by building interpretability into the model from the start, whether it's highlighting decision-making factors or outlining cookie compliance on websites containing sensitive information. This can come at the risk of overall accuracy, though, especially if a model interprets in any of the following ways:

• If parameters aren't set correctly
• If features strongly correlate with each other
• If the model relies on high-dimensional data with a large number of variables

Creating a Solution-Based Approach

In some cases, the best approach can be a hybrid one or even involve interdisciplinary teams. This allows for more control in the creation of AI models and in the final decision-making process. You might even find that it's necessary to solve problems.

Think about banking solutions. Most bank accounts come with AI that lets customers know when there's potential fraud on their account. In some cases, though, the account activity is so suspicious that anti-money laundering models pick up on it. The model might be capable of picking up on suspicious account activity, but more context is required to make a final decision about what to do with the alert.

The data has to be contextualized in order to gain a full understanding of the story that's being told, which is where human intervention is necessary — typically from interdisciplinary teams. Once that context is gained, a hybrid approach with semantic AI can then be brought into the model so that less intervention is needed over time. This is great for strengthening LLM interpretability on large-scale projects.

Final Thoughts

AI interpretability is a crucial part of safe AI development. When you develop models that are intuitive and easy to understand, it's easier for consumers to trust you with more complex models later down the line. Prioritizing interpretability as AI becomes an increasingly common tool among industries is essential for building confidence while mitigating risks and ensuring privacy compliance. The ethical use of AI is still being developed and now is the opportune time to establish your business as among the best when it comes to transparency and overall industry standards.

Zendata can help you do exactly this through privacy integration across data's entire lifecycle. We can help you gain insights into data context and any third-party risks and (above all else) ensure compliance and proper governance with policies and regulations.

If you'd like to learn more about how Zendata can help you incorporate AI interpretability to enhance overall data protection to reduce risks and gain customer trust, you can contact us.