Explainable AI (XAI)
Explainable AI (XAI) refers to a set of techniques and methods that make the decisions and inner workings of artificial intelligence models understandable to humans. This is to address an issue called the “black box”, where people can no longer look at a given output and determine the reasoning to how/why it was generated.
XAI therefore aims to provide transparent, interpretable, and trustworthy insights into how and why an AI model makes certain decisions. The most direct form of XAI in this context is a decision table, where every decision can be traced back to a given if/then statement.
Explainability can be intrinsic (where the model is designed to be understandable) or post-hoc (where interpretability tools are applied after training to explain a complex model's behaviour).
These explanations are crucial for regulatory compliance, ethical accountability, model validation, and user trust.
Transparent AI
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XAI
Examples
Consider a mid-sized Swiss bank that uses a machine learning model to determine whether to approve a customer’s request for a personal loan. The model evaluates factors such as income, employment history, credit score, and past loan behaviour.
First, let’s imagine that they are using a non-explainable or opaque AI model. In the case that a customer is rejected for a loan, the bank must provide a clear reason as required by the EU’s General Data Protection Regulation (GDPR) and Swiss data protection laws. It also needs internal clarity for auditing and risk controls. Lastly, the bank may be at risk or losing customer trust if people start thinking that they are rejected “for no good reason”.
Now let’s imagine that the bank had used an XAI, such as Atfinity’s AI-powered rule engine. In this case, the compliance manager can immediately see which parameter was not satisfied, causing the rejection of the loan request. For example, it was because their risk profile is too high for the risk appetite of this bank.
In this scenario, both the customer and the bank know exactly what caused the rejection. This makes the entire process transparent and fully compliant.
FAQ
Are all AI models explainable?
Not inherently. Some models, like rule engines, are naturally interpretable. Others, like deep neural networks or gradient boosting models, require post-hoc explainability methods to interpret their outputs.
Why is Explainable AI necessary?
XAI is crucial for building trust, ensuring compliance, and validating models. Especially in regulated industries like finance and healthcare, stakeholders need to understand how decisions are made, particularly when they affect people’s lives or money.
Does making AI explainable reduce its performance?
Not necessarily. There is often a trade-off between performance and interpretability, but advances in XAI now allow complex models to be explained without significantly compromising accuracy. In some cases, understanding a model better can even help improve it.
