Heaton commits MoJ to being data-driven
Department's permanent secretary tackles areas such as data sharing and predictive analytics
The Ministry of Justice's (MoJ) permanent secretary Richard Heaton has committed his department to being greater data-driven in the way it operates.
In a blog post , Heaton detailed five ways that the department was "putting data in the driving seat" to enable it to become "smaller, simpler and smarter."
Considering open data and performance, Heaton said he wanted data and evidence to be the engine for reforms to prisons and courts against the background of the need to create a self-improving and accountable justice system with sophisticated performance measurement. That means, he said, having "visible impact indicators - generating real-time, automated information for all to see."
That in turn, he suggested, requires a positive attitude to transparency and openness, and a sensible relationship with risk.
"Neither our performance nor our data will ever be perfect or complete - what is? But the more we publish, and the more we listen to users and to critics, the better we will become. So, expect to see the MoJ releasing more data (to open data standards) and publishing our own performance tools, too."
In terms of unblocking data flows in the justice system, Heaton focused on data sharing, admitting it has long been a contested subject.
"Rightly so; it's important that the rules properly balance privacy and public good. But in a complex network like criminal justice, the whole system could be quicker, more accurate, fairer for victims, and certainly more efficient, if information flowed more easily. Basic stuff like charging decisions, convictions, sentences. Specialised information like offender risk assessments. Data files like video from body-worn cameras.
He went on, "Sometimes, this data flows smoothly between the many different bodies involved. Frequently, it doesn't. The same piece of information often gets recorded more than once, on different systems, by different organisations, in different ways. Or it's passed on manually, mistranslated or corrupted. Most of it is personal data - so scrupulous adherence to data protection law is essential. But across the criminal justice system, we need a fresh approach; we need to recognise the importance of free-flowing data and act on that insight."
Heaton said the MoJ had been speaking with police forces, government departments and the Crown Prosecution Service (CPS) about what the principles of data stewardship should be.
He added, "We are testing the idea that there should be a duty on the creator of data to look after it on behalf of the entire system. A principle that it must be of high quality; accessible to others; and that no charge should be made for access to it. The starting point is not a prescription of technical standards (though they are important), but a simple agreement on terms of trade. Then we can build new platforms with confidence; and we can work out which current data flows are blocked and need attention, and for what reason."
Discussing predictive analytics in government, Heaton said that car insurance can be influenced not just by the car you drive and where you live, but also by your driving style, as recorded by a black box on your dashboard. Advertisers rely on analytics from tech companies to create directly targeted campaigns. Predictive analytics, he said, "is not just big data - it's increasingly big business."
He said, "I have no doubt that the Civil Service needs to catch up. These techniques will help us find better ways of delivering public services, and of shaping policy advice. Algorithms applied to large datasets are going to be useful for operations - to plan flood defences, for example. Comparing individual data against population data will help managers predict and prevent patterns of infection in hospitals, or incidents of violence or self-harm in prisons. You will all be able to think of similar examples.
"Could we go further, and replace human decisions about people's lives with machine learning and predictive analysis? Perhaps the better question is, how far should we go? Getting the ethical framework right is as important as the technical capability; and nowhere is that ethical framework more important than in justice."
He concluded, "But even where we continue to rely on human judgements, we need to be confident enough to know when it is safe to allow data to inform those decisions. We need to know when a predicted pattern is good enough to rely upon, and for what purposes. We need to be clear, ethically, about when predictions are and are not a legitimate basis for action. And we need to be aware that artificial intelligence is capable of eliminating many errors, but might equally reinforce bias."