Preventing Robo-Debt Happening Again


The evidence given by a former member of the AAT is pretty damning.

We can’t claim that mechanising the Social Services legislation as a way of avoiding the Four Pieces Limit will prevent the behaviour described. What we can claim is that creating an authoritative source, where all the meanings of all the words have been clarified and agreed to when the legislation is written, will reduce the possibility of such behaviour occurring in the future. The legislation becomes activatable, so it is no longer just words on a page, the words become nodes and operators in an undirected network – an Active Structure. It can make calculations or deduce conclusions exactly according to the legislation – there is no human involvement by someone insufficiently au fait interpreting the words or trying to turn the words into a program. 

The machine reads the legislation, makes all the necessary connections, and turns it into an active network. The approach is not suitable for handling millions of accounts, but it is suitable for testing theories, handling contested accounts, or debt calculations.

This is not Machine Learning – there is one document to be read, not thousands. The machine has to know the meanings of the words before it started. It is using definitions from a well-known dictionary, extensively curated where the dictionary is inadequate or suffers from circularity.

The techniques of ML, DL, LLM are suited for regurgitation, without any understanding of what the words mean, only that words follow other words on a statistical basis in a particular sort of text. In an Active Structure approach, each word has all the meanings that English gives it – 62 for “set”, 77 for “on”, 82 for “run”. A little tedious to set up, but then visible to anyone who needs help in understanding the legislation – particularly useful where the line staff have little or no legal training.