Artificial intelligence is becoming increasingly embedded in professional services, including the legal sector. As organisations explore how AI can improve efficiency, another question becomes just as important:
Can you trust it?
A convincing demonstration or an impressive accuracy claim doesn’t necessarily tell you how an AI system will perform in practice.
Trustworthy AI requires more than a capable model. It depends on how the system is designed, tested, governed and monitored — as well as how the data it processes is protected.
So what should organisations look for when assessing whether an AI system can be trusted?
1. A Clear Purpose
A trustworthy AI system should start with a clearly defined purpose.
What is the technology designed to do? What isn’t it designed to do? And what role should people continue to play?
These questions matter because different applications of AI carry different levels of risk.
Using AI to organise information, for example, is very different from using an AI-generated output to inform an important legal or clinical decision.
Rather than applying AI simply because the technology is available, organisations should understand the problem it’s intended to solve and whether AI is an appropriate way to solve it.
2. Quality Data
The quality and reliability of an AI system’s outputs can be influenced by the quality of the data used to develop the system and the information it is asked to process.
Incomplete, inaccurate or poorly structured information can affect the usefulness and reliability of the result.
This is particularly important in legal and medico-legal work, where cases can involve significant volumes of complex information and individual details may be important.
Good AI therefore isn’t just about the technology itself. The information being processed matters too.
3. Testing and Validation
Claims about AI performance should be supported by evidence.
Organisations should assess and test AI systems against their intended use and required level of performance. This can help establish how a system performs against the tasks it has been designed to complete, where its limitations lie and where additional oversight may be required.
An accuracy figure on its own, for example, tells you relatively little without context.
How was accuracy measured? What data was used? What was the system being asked to do? And what happens when it encounters information or scenarios outside those tests?
Trust should be based on evidence rather than assumptions about what AI can do.
4. Transparency
Organisations should understand the technology they’re using.
That doesn’t necessarily mean every user needs to understand the technical detail behind an AI model. But organisations should be able to ask meaningful questions about how a system operates and how their information is being handled.
This could include understanding:
- What the AI is being used for
- What happens to information submitted to the system
- Whether data is retained
- Who can access that data
- Whether third parties are involved in processing it
- What safeguards are in place
Transparency helps organisations make informed decisions about whether a system is appropriate for their needs.
5. Security and Data Protection
For legal organisations handling confidential and sensitive information, security is fundamental.
Before adopting an AI system, firms need to understand how information will be processed, stored and protected.
This becomes particularly important when working with medical information, which can contain highly sensitive personal data.
AI shouldn’t be treated separately from an organisation’s wider responsibilities around information security and data protection. The same scrutiny applied to other technology providers should extend to AI systems.
6. Appropriate Human Oversight
AI can support professionals, but that doesn’t mean people should disappear from the process.
The appropriate level of human oversight will depend on the task, the potential consequences of an incorrect output and how the technology is being used.
For complex or higher-risk legal work, appropriate human review is an important safeguard. Technology should support professional expertise rather than remove it.
7. Ongoing Monitoring
Trust isn’t established once and then forgotten.
AI systems and the environments in which they’re used can change over time. Their performance therefore needs to be understood beyond initial testing.
Ongoing monitoring can help identify issues, assess whether the system continues to perform as intended and determine whether additional safeguards or changes are required.
A trustworthy approach to AI considers the entire lifecycle of the technology — not just how well it performs during a demonstration.
Why Trust Matters in Legal Services
The potential benefits of AI in legal services are significant.
Used appropriately, AI has the potential to reduce manual work, process information more efficiently and help professionals make better use of their time.
But the consequences of inaccurate information, poor data handling or inadequate oversight can also be significant.
That’s why firms shouldn’t evaluate AI solely on what it can do or how quickly it can do it.
They should also consider how it works, how it has been tested, how information is protected and what safeguards surround it.
Trust Needs to Be Evidenced
Perhaps the most important principle is that trust shouldn’t simply be claimed.
Statements about accuracy, security, privacy or performance should be supported by appropriate evidence.
For companies considering AI technology, that means looking beyond the marketing and asking the questions that sit underneath it.
What has been tested? How has it been tested? How is data handled? Where does human oversight sit? What happens when something goes wrong?
The answers to those questions provide a much stronger indication of whether an AI system deserves to be trusted.
Building Technology That Supports Better Ways of Working
At MedBrief, we believe technology should make complex medico-legal work more efficient without losing sight of the security, accuracy and expertise it demands.
As AI continues to develop, the companies that use it effectively won’t necessarily be those that adopt it fastest. They’ll be those that understand where it adds value, recognise its limitations and put the right safeguards around its use.