Could crowdsourcing expertise be the future of government?
Recent political events have revealed tensions between expertise and democracy. Institutions must tap into the know-how of the many, not the few
(Originally published in The Guardian on November 30, 2016)
We lack public institutions — a participatory bureaucracy and open parliamentary processes — that know how to tap into the collective intelligence of our communities, and draw power from the participation of the many, rather than the few.
It is the absence of these open institutions, and the resulting failure to take account of the views, voices and know-how of the many disaffected people who voted — and those who did not — during the EU referendum and the US presidential election, which create a vacuum that charismatic demagogues end up filling.
Despite complaints about government dysfunction, Donald Trump has no strategy for how to fix government, save his most recent proposal to kill-two-regulations-for-every-new-one, a page taken from straight from David Cameron’s 2011 “red tape challenge”.
Any progress toward data-driven and evidence-informed policymaking that had been underway in the Obama administration is likely to be systematically rolled back — or at least ignored — under President Trump. There’s nothing to suggest that the White House nudge unit(emulating the UK model), which champions policy experimentation informed by social science research, will survive the chopping block. Nor the data-driven criminal justice initiative that convenes local jurisdictions to commit to empirically measurable reform projects.
Although the first cheques have been written, investments in precision medicine, which rely on massive quantities of data to deliver more targeted care, may not continue. And longstanding open data priorities shared by the US and the UK governments, which have led to the publication of tens of thousands of new datasets, may also be dropped.
None of this is any surprise from a candidate whose presidential campaign was punctuated — and thrived in terms of media attention — by a willingness to play fast and loose with facts.
Of course, the new US administration is not alone in a pervasive contempt for expertise. “We’ve had enough of experts” said Michael Gove infamously, with a recent — and not very comforting — qualification that he was targeting “a sub-class of experts, particularly economists, pollsters, social scientists.” And though there are profound differences between events in the US, UK, Hungary, Austria and France, all display a common thread of anti-elitist, anti-establishment sentiment.
The success of populist candidates highlights a distrust of traditional government institutions that has been percolating for some time. There has long been a conflict between governing by experts and democracy. The history of the 20th century is the history of professionalisation, and the creation in government — and elsewhere — of a governing class that relegated citizens to the role of spectators.
Citizen engagement is largely confined to elections, opinion polls or jury service — asking people what they feel, not what they know and can do — even though democracy should be rule by, for and with the people.
However, this dichotomy between equality and expertise, between democracy and professionalism, is false.
In fact, expertise rooted in lived experience or scientific fact is widely distributed in society. We’ve witnessed a shift away from credentialed experts to citizen experts in everything from restaurant reviews to medical advising. There are many more academic researchers than those who are lucky enough to advise government. Expertise is also not limited to academics nor synonymous exclusively with credentials and the universities of higher education that award them.
So how do we link this distributed expertise to governing? How do we create more participatory institutions?
After all, there is scarcely a public decision, which could not benefit from an infusion of greater expertise — both credentialed and experiential — from outside government. We design the delivery of social services without the benefit of insights from the people who receive that service. We make health policies designed to prevent a pandemic without a clear understanding of what people do and do not know about a disease.
Enter crowdsourcing. Now online tools are making it possible for institutions systematically to get more diverse help and more members of the public to participate in problem solving, by sharing their knowledge and skills.
A recent example is Mapaton CDMX, an effort on the part of twelve organizations in Mexico City to get riders of its informal system of 29,000 microbuses to enter GPS data into a shared database, in order to map 1,500 routes. Over two weeks in February 2016, riders mapped almost the entire system and, with this data in hand, an SMS-based service was developed that allows commuters to enter an origin and destination and get route information.
Crowdsourcing is more than brainstorming. It goes beyond asking people to come up with ideas or supply information. On Amnesty International’s Decoders Network, more than 8,000 volunteers from 150 countries participate in projects to identify human rights violations using satellite photographs.
But the challenge of transferring the success of examples like Mapaton or Decoders to transform public institutions is the limitation of the open call. Those with the greatest know-how often don’t hear about the opportunity, and we can’t govern on the basis of serendipity.
For all forms of engagement to be more effective — whether driven top-down or bottom-up — we need to move to smarter crowdsourcing, which uses technology to make opportunities to participate more visible, and integrates them into how decisions get made.
How do we get there?
First, we have to replicate and scale successful examples. The Smarter Crowdsourcing for Zika project, organized by the Inter-American Development Bank and the Governance Lab, coordinated ministries of health, sanitation, and modernization across four governments in Latin America, for a four-month curated crowdsourcing effort. The project matched hundreds of international experts to specific problems associated with Zika, ranging from trash collection to long term care, for a series of six online conferences designed to inform government responses to mosquito-borne viruses.
Second, we must overcome the assumption that the purpose of engagement is purely to build legitimacy. It is not. If the goal of participation is simply communication between government, citizens and interest groups, then we miss the knowledge building aspects of crowdsourcing. These enable us to find missing information, generate alternate hypotheses, undertake tasks, get more eyeballs on a problem, or boots on the ground.
Third, we should move beyond the assumption that participation must be mass-based. Instead, we should construct a range of different practices that speak to people’s knowledge, experience and passions to spot problems, design policies, work on drafts or participate in implementation.
Fourth, in an era of networks, we must ensure that engagement is no longer limited to interest groups — NGOs, unions, women’s groups — and, instead, look to broader networks of people with innovative ideas to contribute. For the Zika project, representatives of the World Health Organization took part, but so did a researcher from Pakistan, who is using predictive analytics to spot dengue, and a social entrepreneur from Brooklyn, who has designed an app to coordinate school children to pick up trash where water collects.
Finally, there is too little understanding of the models of engagement. We need to accelerate social science research on who participates and why if we are to design effective engagement practices that make government work better.
Will we see a shift to more participatory institutions at the national level over the next four years in the US or the UK? It’s unclear, at best. But at the regional, state and local level, we can and must invest in institutional innovation.
This means more than thrusting a Shoreditch or Silicon Valley techie into an open government role for a tour of duty. By divorcing the idea of expertise from elite social institutions and creating tools to enable neutral identification of talent and ability, technology is democratizing expertise. But we need to train today’s public servants to use these tools to leverage data, unlock talent, and connect motivated innovators inside and outside of government.
Over the next fifty years, we will face challenges greater than any previous generation, and we will need to run our institutions differently. People may not be conversant in the sport of politics, but they do possess expertise in spades. Those who govern need to tap into that know-how, not occasionally, but continuously.