Experts warn that AI systems with high accuracy can still pose risks if they fail to recognise their own uncertainty, emphasising the importance of trustworthiness and transparency in critical sectors like finance and healthcare.
A model that gets 95% of its answers right may still be a poor choice for finance, fraud detection or cybersecurity if it cannot tell the difference between certainty and guesswork. That is the central warning in Ivy Dhanilya’s argument: accuracy shows how often a system is correct, but it says little about whether the system recognises when it is likely to fail.
The National Institute of Standards and Technology makes the same broader point in its guidance on AI risk, saying validation and reliability matter because accuracy alone does not guarantee a system will perform safely over time or under changing conditions. In practice, that means a model can look strong in testing while still producing expensive or dangerous errors in the real world.
The gap between confidence and correctness is just as important. Arun Agrahri has argued that a model’s confidence score can be misleading because AI systems may sound certain even when they are wrong. That is why researchers and practitioners are increasingly focused on uncertainty communication, explainability and calibration, the idea that a system should not only make predictions but also signal how much trust those predictions deserve. A recent review in Springer’s literature on trustworthy AI says those features need to work together with ethical safeguards, particularly in high-stakes areas such as healthcare, finance and autonomous vehicles.
That shift in language also matters. A 2025 paper in AI & Society argues that the aim should not be to make people simply trust AI, but to build systems that are genuinely trustworthy. The distinction is important: trust is a human response shaped by context, while trustworthiness depends on evidence, transparency and robust performance. Put simply, a model that knows when it does not know is far more useful than one that is merely accurate on average.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
