mulgan2_Patrick Pleulpicture alliance via Getty Images_wikipediamonument Patrick Pleul/picture alliance via Getty Images

Special Edition Magazine, Spring 2020: Beyond the Techlash

The Game Changer Beyond AI

For good reason, many people around the world are growing concerned at the speed with which new technologies are replacing labor, disrupting democracy, and manipulating the public for profit. But it doesn't have to be this way: a far superior approach to the design and deployment of technology is already on offer.

LONDON – One might not know it, judging by all the doom and gloom in the press, but there are still parts of the world where technology is regarded as a force for good – even for salvation. A recent survey shows that over 80% of young Africans are optimistic about what technology will do for the continent.

But elsewhere, people increasingly feel as though they are locked in a titanic struggle against technologies that threaten to take their jobs, steal their data, destroy the very idea of childhood, and disrupt democracy. Technology often seems like something that is done to us by distant and unaccountable forces, rather than something that we control. It doesn’t help that, for all of the extraordinary hype about artificial intelligence (AI), much of the investment in this domain has focused on military applications and manipulative ways to target propaganda and advertisements. No wonder people feel vulnerable and anxious.

Fortunately, there are still ways for us to develop a better relationship with the extraordinary technologies that are coming online and to the market. One alternative strategy is to develop “collective intelligence” (CI), which, rather than seeking ways to replace people with AI, focuses on combining the best of humans with the best of machines. This approach is becoming increasingly influential in business, science, and government, partly because it works, but also because it embodies the democratic, humanistic values that many of us hold dear.

Anyone who has ever used Wikipedia will already have a sense of what CI entails. Since the 1990s, millions of people have collaborated online to make reliably accurate knowledge about the world accessible to everyone. In some respects, the wiki model isn’t new. In the nineteenth century, the Oxford English Dictionary recruited tens of thousands of volunteers to map the shifting meanings of English words, using methods similar to those used by Wikipedia today.

CI has begun to take off in recent years because the tools at our disposal have become more powerful than ever. Consider “citizen science” projects like Zooniverse, which have mobilized millions of people online in the search for new stars, the analysis of tumors, and other observational tasks. Advocates of this approach have recognized that there is a huge surplus of brainpower – particularly in highly educated countries – simply waiting to be put to use.

CI is also transforming health care, with thousands of projects bringing patients together to share data or devise better ways to manage disease. One famous project, prompted by a diabetic patient’s frustration with the medical establishment, mobilized volunteers to design a functioning artificial pancreas (insulin pump).

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Following these successes, businesses, too, are looking more closely at CI’s potential. For example, the education app Duolingo uses volunteers to improve its language-learning offerings. Lego, the iconic toy company, has long relied on fans to help it design new products. And the German conglomerate Siemens uses CI methods to organize how it allocates funds internally, on the principle that its engineers will know better than upper management what projects are most likely to succeed.

Finally, government agencies are also tapping into CI’s power. To most people, NASA might conjure an image of hundreds of men in white coats and horn-rimmed glasses in a room in Houston. The US space agency does indeed still employ many scientists, but it has also opened up to the outside world. Whether it is designing a new spacesuit or programming software for rocket launches, NASA will draw on ideas from anyone anywhere, offering financial rewards for those it uses.

The unifying idea here is that by mobilizing data, insights, and ideas from as wide a range of sources as possible, organizations of all kinds will have a better chance of success than if they had relied just on algorithms or on people in house. The best examples of CI combine human brainpower and computers’ processing power, rather than treating each as an alternative to the other. AI and CI in collaboration can often achieve more than either system on its own. In “freestyle chess,” groups of people working with the help of computers have beaten both the best individuals and the best computers.

Combining AI and CI has far-reaching potential. For example, a program based at Swansea University in Wales allows people in Yemen to upload images of munitions debris, which are then classified by machine-learning algorithms to help build cases for the eventual prosecution of war crimes. Similarly, the city of Jakarta combines citizen-generated data on flooding with data from sensors to create a real-time emergency-warning and -response system. And, more recently, similar methods have been used to track the spread of the COVID-19 outbreak.

Democracy itself is now becoming one of the most promising fields for CI. Many experiments have already shown how technology can be used to improve political inclusion and challenge authoritarianism. Here, Taiwan has been a trailblazer, using both algorithms and citizen feedback to assess public opinion and shape policies. Through its new Accelerator Labs, the United Nations, too, has recognized CI as a key to driving progress toward the 2030 Sustainable Development Goals.

Nonetheless, hundreds of billions of dollars will be spent this year on AI for specific, limited tasks such as recognizing faces, formulating product or video recommendations, or winning games of Go. Never mind that most of these applications are themselves heavily dependent on people, whose data is needed to train the algorithms. AI alone is simply not up to the task of dealing with the complex, messy issues that matter most in our daily lives. For these, we need a combination of human and machine intelligence.

Purely technological solutions tend to be overhyped – and then tend to disappoint. The next decade will hopefully be a period when we learn how to use technology to enhance our abilities and not just to replace them.

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