The Importance of Implementing Explainable AI

When developing complex Artificial Intelligence systems, the decision-making processes can often become opaque and turn into “black boxes,” even for the engineers and data scientists who build them. As a result, it can be difficult to understand why a model has made a particular decision, a challenge that has serious implications, especially for national services that support critical national infrastructure across healthcare, transport, civil defence, and environmental management.

As data scientists, our role is to develop models that are accurate, efficient, and fair. However, when the AI systems are deployed into production environments, the importance of explainability increases, especially when a system’s decision-making can impact people’s lives. Helping our clients trust and understand the AI’s decision-making process is critical, particularly in highly regulated industries where ethical considerations are mission-critical. Equally, business leaders, product managers, and policymakers all need to be able to explain and justify the outcomes of AI models to customers, regulators, and the public, ensuring that AI systems are developed and used ethically and responsibly.

A lack of transparency can lead to reduced trust, legal complications, and unintended biases, negatively impacting both individuals and organisations.

In this piece, we explore how we use Explainable AI (XAI) techniques at Informed Solutions and how they can be leveraged to encourage a culture of transparency.

Why is Explainable AI (XAI) Important?

High Value, Trust Based Innovation

Users need to be able to trust the recommendations and decisions produced by AI systems to use them effectively, and if a user is unable to understand how an AI arrived at its conclusion, they are less likely to rely on it.

This lack of trust can have serious consequences. In healthcare, for instance, doctors may be hesitant to act on an AI’s diagnosis if they don’t understand the reasoning behind it, potentially leading to missed or incorrect treatments. Moreover, when users don’t trust AI systems, they are less likely to adopt them, which limits AI’s broader impact across industries. This can hinder innovation, prevent businesses from realising operational efficiencies, and slow down the transformation of industries that AI has the potential to revolutionise. Without XAI, therefore, the potential value of AI tools could be compromised.

Regulatory Compliance

Regulations such as GDPR mandate that AI-driven decisions be interpretable, especially in high-risk domains. Companies that fail to ensure AI explainability may face legal consequences, financial penalties and reputational damage, so it is essential that data scientists consistently uphold XAI as a standard when working on new systems.

Identifying Bias

The data used to train an AI model may contain biases, and if the decision-making process isn’t transparent, it becomes challenging to detect and correct discriminatory patterns. As such, Explainable AI helps organisations audit their models, identify and adjust biases, ensure fair outcomes and reduce the risk of harm from decision-making.

Improving Performance

AI models can sometimes generate unexpected outputs, and when they lack transparency, diagnosing and fixing these errors becomes difficult. Explainability, therefore, allows developers and data scientists to trace errors, refine model performance, and continuously improve system accuracy.

Five Strategies to Improve Explainability

1. Choose Interpretable Models

Some AI models are inherently more interpretable than others due to their simple rules and relationships. Decision trees, for instance, are models that make decisions by splitting data into different branches based on certain rules. It’s like a flowchart where each node (or “question”) divides the data into smaller, more specific groups based on the values of the features (like age, income, etc). These decisions continue until the tree reaches a “leaf”, where a final decision or prediction is made.

At Informed, we follow a philosophy akin to Occam’s Razor—choosing the simplest, most interpretable model that still meets the required level of accuracy. For instance, when tackling a straightforward binary classification problem, we would favour a classical machine learning technique, such as logistic regression, over a more complex deep neural network, provided it delivers sufficient predictive performance.

2. Implement Explainability Techniques

For complex models, XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can provide insights into AI predictions by showing the ‘why’ behind the prediction or recommendation.

SHAP values, for instance, help explain how individual features influence a model’s prediction. In simple terms, SHAP assigns a “weight” to each feature, showing you exactly how much each feature (like “age” or “income”) influenced the final decision. This method is based on a concept from game theory, where each player’s contribution is calculated in a fair way.

LIME, on the other hand, works by creating a simple, interpretable model that mimics the behaviour of a more complex model for a specific instance or prediction. For example, if you’re trying to figure out why an AI recommended a specific product to you, LIME can create a simplified version of the AI for that one recommendation and show you which parts of your preferences (such as past purchases) were most influential in the decision.

At Informed, some of the problems we tackle involve complex relationships that necessitate the use of “black box” deep learning models. In these cases, interpretability techniques like SHAP and LIME enable us to look beneath the surface and better understand the factors driving specific predictions, helping us maintain transparency even with more advanced models.

3. Establish Transparent AI Governance

Organisations should establish AI governance frameworks that define clear guidelines for transparency. For instance, they should include guidance on documenting model development processes, as well as maintaining audit trails and ensuring explainability standards are met across AI implementations.

At Informed, our AI Charter commits us to putting ethics, safety, responsibility, and security at the core of everything we do. This means designing AI solutions that are safe, transparent, robust, and fair from the outset. Explainability plays a central role in upholding these values across our organisation. To support this, our “Well-Assured Framework” provides a comprehensive checklist that guides our team through key considerations, ensuring every solution we deliver is trustworthy and aligned with our principles.

4. Provide User-Friendly Explanations

AI explanations should be tailored to their audience. For example, a data scientist may need in-depth mathematical insights, whereas an end-user would require simpler, more intuitive explanations. Creating role-specific transparency, therefore, ensures that all stakeholders can meaningfully interact with AI systems.

Because we work closely with our clients, it’s essential that we communicate our models in a way that’s accessible and easy to understand. By prioritising explainability from the outset of a project, we’re able to offer clear, concise overviews of how models are developed, ensuring our clients remain informed and confident in the solutions we deliver.

5. Conduct Regular Audits

By setting up proactive alerts and tracking key indicators, such as shifts in data distributions, unexpected model behaviour, or signs of bias, we can respond promptly and appropriately. This not only helps maintain the integrity and performance of our models over time but also reinforces our commitment to delivering responsible and trustworthy AI solutions that adapt as real-world conditions change.

Conclusion

As AI usage becomes ubiquitous and continues to shape critical decision-making processes, ensuring explainability and transparency is a necessity. Organisations that prioritise XAI principles will not only comply with regulatory requirements but also build trust, mitigate risks, and enhance AI performance. Therefore, by adopting these practices, we can continue to ensure that AI systems are transparent, reliable, and more widely trusted and adopted.