How can governments generate greater trust when it comes to data? Nicola Eschenburg says it can be done, and the sooner the better
Trust. Rarely does one word resonate so strongly across society. It affects all of us in different ways. It’s something that we, as individuals, yearn for. It’s something we want to generate – with each other, with our family, with our friends and with our colleagues. And it’s something we want to enjoy with all the groups and organisations we interact with.
But the thing about trust is that it doesn’t happen overnight. There’s that oft-repeated phrase about it taking “years to build, seconds to break and forever to repair”. Now, I’m not sure if I agree with that entirely – I wouldn’t instantly stop trusting my best friend if she forgot to meet me for a coffee – but it’s certainly true that it’s easier to lose trust than it is to create it.
And government is no exception.
Mapping government data
Wherever we are in the world – from my native Zimbabwe to my home in the UK – we want to be able to trust government to fulfil its constellation of roles, from ensuring basic law and order to protecting citizens to delivering public services.
Unfortunately, it doesn’t always work out that way – particularly when it comes to data. As my colleague Alex Richards has pointed out, “data is the lifeblood of pretty much everything we do” but, alas, policymakers do not have a flawless track record to call upon.
In 2018 in India, for instance, several people died of hunger after their welfare benefits were tied to their Aadhaar ID number, a biometrics identification system. And here in the UK, the Public Administration and Constitutional Affairs Select Committee recently warned that the government had failed to sufficiently explain data underpinning key decisions during the pandemic – damaging public confidence as a result. And these are just two examples and there’s plenty more out there to draw upon.
But perhaps I’m being unfair. Government uses data for the public good every day – the vaccination programme spring to mind. And it’s not as if the private sector has much to boast about either.
After all, it’s easy to bash government because, thanks to select committee reports like the one above, it’s pretty transparent. By contrast, I don’t know how my bank, for example, uses my data, nor my broadband provider or airline loyalty programme. When looking across sectors we see the same problems keep popping up, such as an obsession with new technologies, siloed working, too much jargon – the list goes on.
The three layers of trust
What we need, then, is a way of understanding how trust can take root, particularly when it comes to data and its cascading uses across society. With algorithms holding increasing sway over how decisions are made and what we all do, we have identified three layers to help better understand trust and how to build, nurture and understand it.
The first is dispositional. By this we mean an individual’s enduring tendency to trust automation based on factors such as culture, age and personality. The second is situational, meaning the specific context and this is based on aspects such as an individual’s ability to freely evaluate an algorithm’s performance. And the third is learned which is based on past experiences relevant to specific system and its ongoing performance. Here, factors include pre-existing knowledge, design features, performance and situational factors not related to trust.
These layers are applicable across both government and the private sector but there’s little doubt that policymakers have much to do. The OECD found that a mere 45 per cent of citizens trusted their government in 2019, a figure that raises doubts over citizens’ willingness to support public policies and contribute to a sustainable post-pandemic recovery.
Governments, though, can take solace in the knowledge that there are plenty of ways that they can improve their handling of data and, as a consequence, generate greater citizen confidence.
For example, they can invest in learning and development by establishing a culture where the majority of their workforce is confident about using data. Data leaders need to think about how their people can be champions for data confidence, inspiring change and making data accessible.
Policymakers also need to remember that data doesn’t have to be scary. Data can range from a highly complex dataset used to create an algorithm but it could as simple as your date of birth. In other words, it’s important to humanise data – a process which can fall to the increasing cadre of data professionals across government departments.
In summary, then, data need not fuel mistrust of governments. On the contrary, handled well it can build confidence and, in doing so, help bridge the divide between voters and politicians.
We don’t need an algorithm to tell us that would be a good outcome.
Find out more about our Futures team. Driving innovation from within.
About the author
Nicola Eschenburg, FinCrime Testing Service Venture Lead in the Futures team at BAE System Applied Intelligence
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