A New Society, a new education!

Augmented collective intelligence depends on technological amplification of the ability of humans to think, communicate, and work together -- and upon the norms, methods, and institutions that humans create to overcome social dilemmas. Wikipedia's Neutral Point of View is a perfect example of this kind of techno-social arrangement.

Sobre los límites de la inteligencia colectiva.

We examine whether collective intelligence helps achieve a neutral point of view (NPOV) using data from Wikipedia’s articles on US politics. Our null hypothesis builds on Linus’ Law, often expressed as “Given enough eyeballs, all bugs are shallow.” Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law. Moreover, several empirical facts suggest the law does not shape many articles. The majority of articles receive little attention, and most articles change only mildly from their initial slant. Our study provides the first empirical evidence on the limit of collective intelligence. While many managers believe that they could improve their products by taking advantage of the wisdom of crowds, we show that in the case of Wikipedia, there are aspects such as NPOV that collective intelligence does not help achieve successfully.

Abstract:      
Algorithms have developed into somewhat of a modern myth. They “compet[e] for our living rooms” (Slavin 2011), “determine how a billion plus people get where they’re going” (McGee 2011), “have already written symphonies as moving as those composed by Beethoven” (Steiner 2012), and “free us from sorting through multitudes of irrelevant results” (Spring 2011). Nevertheless, the nature and implications of such orderings are far from clear. What exactly is it that algorithms “do”? What is the role attributed to “algorithms” in these arguments? How can we turn the “problem of algorithms” into an object of productive inquiry? This paper sets out to trouble the coherence of the algorithm as an analytical category and explores its recent rise in scholarship, policy, and practice through a series of provocations.

Keywords: algorithms, governance, automation, computation, big data, sociology, law, public policy, control