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Geoff Mulgan wants us to rethink how to use AI technology

Humans and AI are working together to solve some of the world’s biggest problems. Here’s how to make them work even better

Director of Innovation Strategy, International Rescue Committee
Chief Innovation Officer, International Rescue Committee

Ensuring that refugees are resettled in places where they will be most likely to get a job, predicting the onset of conflicts and epidemics, and measuring the cumulative impact of local development interventions are tough humanitarian challenges that have started to look a lot easier to solve, thanks to advances in artificial intelligence. But Geoff Mulgan says that unless we rethink how humans harness the capacity of those new technologies, we won’t be much better off.

Mulgan is the current head of the UK innovation hub Nesta and a former policy head in Tony Blair’s government. He’s also the co-founder with the Dalai Lama of Action for Happiness, holds a Ph.D. in telecommunications, and studied to be a Buddhist monk in Sri Lanka as a teenager.

Mulgan is concerned with “the space between the individual and the totality of civilization”; in other words, organizations, and how they function – or malfunction. In his most recent book, Big Mind: How Collective Intelligence Can Change Our World, he lays out a set of guidelines for organizations to structure human capacity to make more effective use of growing digital capacity. His focus is on how to fuse elements of organizational learning and network theory into a mechanism for harnessing the power of collective intelligence – or how to synthesize and act on information from myriad different sources in a way that’s efficient, effective, and equitable.

Geoff Mulgan Photo: Kennisland

And he does not ask small questions. “We have extraordinary intelligence in pockets, for specific, defined, tasks. Yet there has been glacial, if any, progress in handling more complex, interconnected problems, and paradoxically the excitement surrounding new capacities to sense, process or analyze may distract attention from the more fundamental challenges,” he writes in Big Mind. In other words, why are some organizations, even those full of smart, capable, people better than others at navigating the “uncertain currents of the world around them”? And what are the best ways for societies, governments, and organizations to solve complex problems?

Perhaps the most commonly-cited success of collective intelligence is the elimination of smallpox in the middle of the last century. In 1959, 63 countries reported close to 80,000 cases of smallpox – and it soon became clear that as few as 1 in 100 cases were being reported. Wiping smallpox from the globe, therefore, depended on globalized networks of collective intelligence to gather accurate, complete information and adaptive strategy in light of experience.

Delays in delivery of supplies and smallpox outbreaks in groups that had resisted vaccination, for example, forced the task force to change its strategy from mass vaccination to surveillance and containment. Country teams established radio networks and reporting incentives that could quickly pinpoint new smallpox cases and send containment teams to isolate infected persons and vaccinate surrounding villages. The grassroots collection of intelligence feeding into diffused systems of action, operating with a common goal and methods, was able to break the transmission of smallpox, leading to the global eradication of the disease only ten years after the program began.

How does intelligence happen at a large scale, in an organization, or a city, or a country, or a community? If we imagine it as a brain, how would it think, and how could it better solve its problems?

It seems logical that in the digital era, organizations wielding powerful computerized algorithms should be able to respond even more quickly and effectively to large-scale problems. Mulgan argues that hasn’t always been the case: When smart technology is placed in inept systems for making decisions, the consequences can look like the 2008 financial crisis, where bankers collectively disregarded complex problems. But more happily, there are dozens of institutions that have tapped the “possibilities of a bigger mind” by teaming human discernment with machine analytics.

Local governments, for example, are growing adept at using the tools of the data commons to better address citizen needs, and there are digital tools in the works to make it even easier to detect and control new epidemics. “If there are rewards for people … to spot potential sources of outbreaks, then you feed that into predictive algorithms using artificial intelligence to predict how they’re likely to spread. Looking at patterns of transport and mobility, then you feed them into public health systems so they can act preemptively before the epidemic spreads,” Mulgan says.

This is a conversation about one of the biggest ideas at the frontier of the humanitarianism, and it’s one you need to listen to if you want to understand how the sector will change in the years to come.

Related Reading

Big Mind by Geoff Mulgan – the power of collective intelligence – Financial Times, James Crabtree

This study tried to improve our ability to predict major geopolitical events. It worked. – Vox, Zack Beauchamp

This algorithm can predict a revolution – The Verge, Russell Brandom

Garry Kasparov: “Deep Thinking” – Talks at Google

How artificial intelligence might help achieve the SDGs – Devex, Catherine Cheney

An Algorithm for Refugee Resettlement Could Boost Employment and Integration – Immigration Policy Lab

Pandemic simulation used in Gates Shattuck Lecture – Institute for Disease Modeling

Opinions and views expressed by guests are their own and do not reflect those of the International Rescue Committee.