Why explainable AI matters legally and how to make it work in practice
If your business uses AI to help make decisions , whether that is about customers, staff, risk or eligibility — you may already be facing regulatory obligations you are not fully aware of. One requirement that catches many small and growing businesses off guard is explainability: the ability to show what your AI system did, why it did it, and how it reached its conclusions. This article explains what that means in practice and what steps you can take now.
At Thrive Law, we regularly support clients who are surprised by just how important explainability and audit trails really are. They are not only about technical accuracy. They are about fairness, accountability and ensuring that people understand how decisions affecting them are made. If your organisation is using AI in any way that relates to people, this is becoming a key priority.
Why Explainable AI Matters Legally
Explainability is central to lawful and responsible AI. When an AI system influences a decision about a person, there must be a clear understanding of how the decision was reached. This applies whether the decision is about recruitment, risk assessment, customer eligibility, prioritisation or something else entirely.
The legal obligations are strongest where decisions are made solely by automated means and have a significant effect on an individual. Under the UK GDPR, as now updated by the Data (Use and Access) Act 2025, organisations must be able to provide meaningful information about how such decisions work. But even where a human is involved, regulators expect you to be able to demonstrate that you understand and can account for what your AI is doing.
Regulators expect organisations to be able to show that decisions are:
• Fair
• Consistent
• Non-discriminatory
• Grounded in understandable logic
If your teams cannot explain what happened or why it happened, it raises concerns about whether the organisation has meaningful control over its systems. It also makes it difficult to respond to complaints, disputes or challenges from individuals.
Explainability is not about providing technical details or mathematical formulas. Instead, it is about offering a simple explanation of:
• What influenced the decision
• Which factors mattered most
• How those factors were weighed or considered
When organisations can do this well, it builds trust and reduces the risk of misunderstandings or disputes.
Audit Trails: Evidence That You Are in Control
Audit trails are one of the most effective ways to demonstrate responsible AI use. They show your organisation’s journey from input to outcome, capturing what happened at the moment a decision was made.
A strong audit trail usually includes:
• The datasets and variables used
• The version of the model running at the time
• Which factors influenced the outcome
• Any human involvement or overrides
• Monitoring, testing and validation carried out
• How often the model is reviewed or updated
These records are incredibly helpful. Not only do they offer reassurance during regulatory inspections, but they also help internal teams check consistency, spot errors, and identify whether a model might be drifting or becoming less accurate.
In practice, an audit trail can act as a safety net. If a customer challenges a decision or a regulator asks for evidence, your documentation provides clarity and certainty. Without this, organisations can find themselves struggling to demonstrate accountability.
The Myth of AI Being Too Complex to ExplainExplainability is not optional because your AI is complex
Many small businesses assume that because their AI system is supplied by a third party or operates as a ‘black box’, they are not responsible for explaining it. Regulators take a different view. Complexity does not remove the obligation to explain decisions — it heightens the need for you to understand what you have deployed.
You do not need to explain every technical detail. But if something goes wrong and you cannot explain what your system did or why, that is a governance failure. It may also be a data protection breach. If a model is so complex that no one in your organisation can understand it, that is a signal that you need better documentation, more training, or a different approach.
How to Build Explainability Into Your AI FrameworkProcesses
Explainability is not something that can be added on at the end. It needs to be considered at every stage of the AI lifecycle. The most effective organisations incorporate it into their processes, culture and governance structures from the beginning.
Design with transparency in mind
When creating or sourcing an AI model, consider how easy it will be to explain its behaviour to others. Documenting decisions, assumptions, strengths and limitations from the start helps ensure clarity later on.
Use practical tools that support visibility
Technology that helps teams interpret outputs can make explainability far more manageable. This might include:
• simple dashboards
• variable importance charts
• audit logs
• explanation tools This might include simple dashboards, variable importance charts, audit logs or explanation tools built into the system.
These tools help transform complex model logic into clear, people friendly plain language .information.
Strengthen human oversight
AI should support decision making, not replace it entirelymake them unchecked. Staff should understand what the system does, be trained to challenge AI its outputs, offer alternative reasoning and override decisions it when appropriate. This helps maintain fairness and avoids blind reliance on automation.his is not just good practice but increasingly a regulatory expectation.
This might include simple dashboards, variable importance charts, audit logs or explanation tools built into the system. These translate complex model logic into plain language that people can actually use.
Invest in Practical Training
Training plays a crucial role in helping teams feel confident and in control when working with AI. It should go beyond the technical aspects and focus on giving people the skills to understand and communicate how the model behaves in real situations. This includes developing a practical grasp of how the system works day to day, so staff can recognise which factors influence an outcome and where human judgement is still needed. For SMEs, training does not need to be elaborate. The goal is to ensure the people using AI tools can explain, in plain language, what the system does and why a particular output was reached. They should also be able to spot when something looks wrong and know what to do about it. A short, focused session that covers how your specific system works in practice will achieve far more than a generic e-learning module.
It is equally important that employees can explain AI-supported decisions clearly and calmly, whether they are speaking to a regulator, a customer or an internal colleague. Being able to translate complex processes into simple, reassuring language helps build trust and strengthens accountability.
Training should also help teams identify when something does not look right, including unusual patterns or potential errors that might indicate a problem with the model. When people feel confident asking questions, challenging outputs and raising concerns, the entire organisation benefits from a more responsible and transparent approach to AI governance.
A Practical Example
Imagine an organisation that uses AI to support credit assessments. With the right approach, it could:
• Record which variables influenced the decision each time
• Allow human reviewers to override outcomes with clear justification
• Explain the reasoning to customers in a supportive way
• Document fairness checks, risk assessments and model updates
• Store historic versions of the model and track changes
This not only makes the decision-making process clearer but also builds confidence for customers and regulators. It shows that the organisation is taking responsible AI seriously and that people remain at the centre of the process. Consider an SME that uses an AI tool to assess customer eligibility for a payment plan. Without proper governance, a rejected customer may have no clear explanation for the decision, and the business may have no record of how the outcome was reached.
With the right approach in place, the same business could:
- Record which factors the system used each time a decision was made
- Allow a team member to review and override the outcome witha clear reason noted
- Explain the decision to the customer simply and supportively
- Demonstrate to a regulator that checks and reviews are carried out regularly
This is not a large-scale compliance project. It is a set of habits and records that any business can build with the right guidance.
Why This Matters More Than Ever
As AI use continues to grow, regulators are focusing strongly on accountability, transparency and fairness. Organisations that invest in explainability and auditability now will find it far easier to demonstrate responsible use, respond to questions and adapt to future regulatory changes.
On a practical level, these processes also improve internal understanding, reduce risk and help teams make better decisions. They give organisations the confidence to innovate safely and responsibly.
Conclusion
Explainability and audit trails are not just about compliance. They are essential tools for building trust, strengthening decisions and protecting individuals. When organisations understand and document how their AI works, they create systems that are fair, transparent and resilient.
If your organisation needs help creating stronger AI governance, developing explainability standards or training your team, we are here to support you.
You can reach Thrive Law at enquiries@thrivelaw.co.uk. We would be happy to help you build AI practices that are safe, responsible and future ready
