Guest blog by Donna Phillips, Chair of the RSS AI Task Force
Artificial intelligence is often talked about as if it can think like a person. We hear that it understands, reasons and even creates. But AIs think quite differently to how people think: they are fundamentally statistical. This is a fact that is not widely understood – but I believe that it is an essential point that needs far greater recognition for AIs to be used effectively, safely and ethically.
Large language models (LLMs), the systems behind many chatbots and search tools, are trained on vast amounts of text and data. They look for patterns in that data and use those patterns to predict what is most likely to come next. When they produce an answer, they are not thinking about it in a human sense. They are generating the most likely response based on what they have seen before.
This is what makes them so impressive. It is also why they sometimes go wrong.
Because these systems are statistical, their outputs depend on the data they have been trained on. If that data contains gaps or biases, the results will reflect that. If the system is used in situations that differ from its training data, its performance can change. And even when an answer sounds confident, it is still based on probability rather than certainty. Understanding this helps us use AI more wisely.
It encourages simple but important questions. Where did the data come from? How representative is it? How reliable is the output? How might results differ for different groups of people? What happens when circumstances change?
These questions matter when AI is used to support decisions about jobs, loans, healthcare, education or public services. As AI becomes more common in everyday systems, basic statistical awareness becomes part of digital knowledge.
This is why, led by its AI Task Force, the RSS has published a landmark paper on the statistical nature of AI. Our core argument is clear: AI systems are built on statistical pattern recognition. They need to be developed, evaluated and governed with rigorous statistical precision.
That has implications for education. Young people learning about AI should also learn about data, uncertainty and bias. It also has implications for government. Civil servants using AI tools should be supported to question and interpret outputs, not just rely on them. It has implications for regulation. Public bodies need the expertise to assess claims about accuracy and reliability. And it has implications for those building and deploying AI systems. Strong evaluation and ongoing monitoring are essential.
None of this means AI is something to fear. Nor does it mean it should be embraced without question. It means recognising how these systems work and applying appropriate scrutiny. AI will continue to develop. Public debate will continue. A clear understanding of the statistical foundations of these tools provides a steadier basis for all of it.
AI is statistics. Recognising that helps us use it responsibly.
Read the full paper, AI is Statistics: Why statistical thinking is vital for the effective, ethical and safe use of AI.