Explainability: Understanding Why a Model Decided
When AI influences decisions about people, you often need to explain why. Explainability is about making a model's reasoning understandable — to you, your customers and regulators.
This article explains why it matters and what is realistic.
Why It Matters
- Customers deserve a reason for decisions affecting them.
- Regulations may require it.
- It helps you spot and fix unfair or wrong behaviour.
- It builds trust with users and staff.
The Honest Limits
Simple models are easy to explain; the most powerful ones are not, and any explanation is a useful approximation rather than the whole truth. Sometimes a slightly less accurate but explainable model is the better business choice.
Our Approach
We aim for the right balance between power and transparency for each situation, rather than reaching for the most complex model by default.
- Match the model's complexity to the need for explanation.
- Record the main factors behind each decision.
- Provide plain-language reasons where people are affected.
Frequently Asked Questions
Does explainability reduce accuracy?
Sometimes a slightly simpler, explainable model is marginally less accurate — but for decisions about people, that trade-off is usually worth it.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.