Background to the commons-based design principles

Here we introduce several core principles that we think are likely to be fundamental to building a commons-based model of data integration and reuse.

The community design principles here build upon those first developed in the New Zealand Data Futures Forum (NZDFF) public consultation process.

The first NZDFF paper illustrated the balance between risk (fear of adverse consequences of increased data sharing) and value (desire to do more to improve lives). Too often this was seen as a dilemma: that protecting privacy or being risk averse necessarily squanders significant opportunities to realise sometimes life-saving value; or that to grab the value leads necessarily to trampling on human rights (including privacy) or commercial sensitivities. The NZDFF challenged New Zealand to hold both of those principles together at the same time. It argued that doing so was the only way towards the kind of data sharing ecosystem that we were aiming for – one that both managed risk and realised value.

With this in mind, the NZDFF came up with four design principles for a safe and high-value data sharing ecosystem.

Two of these principles were focused on keeping the value side of the equation at the table:

(1) the data sharing ecosystem needs to direct value from data sharing back to its participants; and
(2) it needs to be inclusive, thus providing shared value, not monopolised by partisan interests.

Two principles were focused on keeping risk-control at the table:

(3) the data ecosystem has to be high trust (I have confidence that the system will protect my interests); and
(4) the system must be in the control of its participants (data sharing is something you do, it isn’t done to you).

It was also concluded that if you achieve these four design principles for data sharing, that, far from being a dilemma, the result is a positive feedback loop. The more you enable value and inclusion, the greater risks people are likely to take to do more data sharing, because they themselves get that value from it. By the same token, the more you improve control and trust, the more people will be willing to try sharing to realise some potential value, because they are still in control and can reverse their decision if trust is eroded.

But this feedback loop can also spiral downwards. If you erode trust, people will cease sharing, meaning loss of value, making people more sceptical and so feeding back into increased mistrust – because the perceived risk outweighs value. That is basically the system dynamic that underpins the “Ownership Model” and why it tends towards fragmentation and mistrust.

You have to consider both sides of this equation together to build a thriving data sharing ecosystem.

These four principles are at the heart of creating (or destroying) a thriving data sharing ecosystem. The important point to grasp here is that it is not a dilemma, it’s a feedback loop. You have to consider both sides of this equation together to build a thriving data sharing ecosystem. If you just think of value without trust, this will unravel. If you just focus on risk, you end up unable to realise value and so remain sceptical.

These principles were widely applauded by the likes of the UN Global Pulse and the Privacy Commissioner who thought they added significant new thinking to working past the old confrontational debate between risk and value. However, the NZDFF did not have the time to figure out the next step: how to actually apply these in practice.

Further thinking was done in “Handing Back the Social Commons”. Here it was argued that to apply the NZDFF principles required thinking about the way data could be managed as a common-pool resource rather than owned: additional data sharing activity could learn from the notion of common-pool interests and how these are managed.

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