4 November 2019
The use of algorithms in public sector decision making has broken through as a hot topic in recent weeks. The Guardian recently ran the “Automating Poverty” series on the use of algorithms in the welfare state. And on 29 October 2019 it was reported that the first known legal challenge to the use of algorithms in the UK, this time by the Home Office, had been launched. It was timely, then, that the Public Law Project’s annual conference on judicial review trends and forecasts was themed “Public law and technology”.
Basic tech for lawyers
The conference helpfully opened with a lawyer-friendly run down of algorithms and automation. Dr. Reuben Binns (ICO Postdoctoral Research Fellow in AI) drew a number of useful distinctions.
The first was between rule-based and statistical machine learning systems. In rule-based systems, the system is programmed to apply a decision-making tree. The questions asked and the path to a particular outcome, depending on the answers given, can be depicted by way of flow-chart (even if that flow-chart might be very large, involving numerous branches). In contrast, statistical machine learning involves a computer system training itself to spot patterns and correlations in data sets, and to make predictions based on those patterns and correlations. The computer system is first trained on data sets provided by the system designer. Once trained, it can be used to infer information and make predictions based on new data. These systems might be used, for example, to assess the risk of a person re-offending, where the system has been trained on existing data as to re-offending rates. It has long been known that machine-learning systems can be biased, not least because the data on which they are trained is often biased.
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