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The Emerging Science of Human Computation

The Web has turned the wisdom of the crowd into a valuable, on-demand resource. Now scientists are asking how best to put crowdsourced cognition to work.

The wisdom of the crowd has become so powerful and so accessible via the Internet that it has become a resource in its own right. Various services now tap into this rich supply of human cognition, such as Wikipedia, Duolingo, and Amazon’s Mechanical Turk.

So important is this resource that scientists have given it a name; they call it human computation. And a rapidly emerging and increasingly important question is how best to exploit it.

Today, we get an answer of sorts thanks to a group of computer scientists, crowdsourcing pioneers, and visionaries who have created a roadmap for research into human computation. The team, led by Pietro Michelucci at the Human Computation Institute, point out that human computation systems have been hugely successful at tackling complex problems from identifying spiral galaxies to organizing disaster relief.

But their potential is even greater still, provided that human cognition can be efficiently harnessed on a global scale. Last year, they met to discuss these issues and have now published the results of their debate.

The begin by pointing out the extraordinary successes of human computation. One of the most notable is the project in which participants are asked to fold virtual proteins in the most efficient way possible. The goal is to solve one of the most important outstanding problems in molecular biology: how proteins fold so rapidly and efficiently.

The project has had some impressive successes. Soon after it began, it discovered the tertiary structure of a regulatory protein for the pros-simian immunodeficiency virus, a problem the research community had puzzled over for decades and one that that could lead to new ways of tackling the AIDS virus.

And the Zooniverse project asks citizen scientists to identify craters on the moon, help translate old ships’ logs, identify galaxies in astronomical images and find planets around other stars, among many other things.

Michelucci and co then describe the kinds of projects they want to create. They call one idea Project Houston after the crowdsourced effort on the ground that helped bring back the Apollo 13 astronauts after an on-board explosion on the way to the moon.

Their idea is that similar help can be brought to bear from around the world when individuals on earth find themselves in trouble. By this they mean individuals who might be considering suicide or suffering from depression, for example.  

The plan is to use state-of-the-art speech analysis and natural language understanding to detect stress and offer help. This would come in the form of composite personalities made up from individuals with varying levels of expertise in the crowd, supported by artificial intelligence techniques. “Project Houston could provide a consistently kind and patient personality even if the “crowd” changes completely over time,” they say.

Another idea is to build on the way that crowdsourcing helps people learn. One example of this is Duolingo, an app that offers free language lessons while simultaneously acting as a document translation service. “Why stop with language learning and translation?” they ask.

A similar approach could help people learn new skills as they work online, a process that should allow them to take on more complex roles. One example is in the field of radiology, where an important job is to recognize tumors on x-ray images. This is a task that machine vision algorithms do not yet perform reliably.

Humans are good at this, however. A novice could begin by looking at images that are easy to classify and then progress onto more difficult cases when they have demonstrated a certain level of proficiency. “We believe that online learning that doubles as work (and vice versa) can have a transformative impact on the future of work and education,” say Michelucci and co.

Yet another idea would be to crowdsource information that helps the poorest families in America find social welfare programs. These programs are often difficult to navigate and represent a disproportionate hardship for the people who are most likely to benefit from them: those who are homeless, who have disabilities, who are on low income, and so on.

The idea is that the crowd should take on some of this burden freeing up this group for other tasks, like finding work, managing health problems and so on.

These are worthy goals but they raise some significant questions. Chief among these is the nature of the ethical, legal, and social implications of human computation. How can this work be designed to allow meaningful and dignified human participation? How can the outcomes be designed so that the most vulnerable people can benefit from it? And what is the optimal division of labor between machines and humans to produce a specific result?

Michelucci and co outline these and other questions that they say will be crucial for the emerging science of human computation.

No study of this kind would be complete without an analysis of the funding environment.  Michelucci and co end by calling for a new national initiative in human computation with the creation of a national center devoted to this emerging science.

A national center in the U.S. certainly seems like a good idea. Human computation is multidisciplinary field that requires expertise in computer science, complexity science, cognitive sciences, behavioral economics, human-computer interaction, and so on.

So having a place where researchers from these fields can meet and work together seems an obvious step forward.

Ref: : A U.S. Research Roadmap for Human Computation

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