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Viewed as a form of social organization, human-based computation often surprisingly turns out to be more robust and productive than traditional organizations.<ref>{{cite web |url=https://web.archive.org/web/20110707063732/http://research.3form.com/alex/pub/gecco-2002-18.pdf |title=Evolutionary Computation as a Form of Organization|author=Kosorukoff, Alexander, and David Goldberg |date=2002|access-date=12 May 2022}}</ref> The latter depend on obligations to maintain their more or less fixed structure, be functional and stable. Each of them is similar to a carefully designed mechanism with humans as its parts. However, this limits the freedom of their human employees and subjects them to various kinds of stresses. Most people, unlike mechanical parts, find it difficult to adapt to some fixed roles that best fit the organization. Evolutionary human-computation projects offer a natural solution to this problem. They adapt organizational structure to human spontaneity, accommodate human mistakes and creativity, and utilize both in a constructive way. This leaves their participants free from obligations without endangering the functionality of the whole, making people happier. There are still some challenging research problems that need to be solved before we can realize the full potential of this idea.
The algorithmic outsourcing techniques used in human-based computation are much more scalable than the manual or automated techniques used to manage outsourcing traditionally. It is this scalability that allows to easily distribute the effort among thousands of participants. It was suggested recently that this mass outsourcing is sufficiently different from traditional small-scale outsourcing to merit a new name [[crowdsourcing]].<ref>{{cite
Human Computation organizes workers through a task market with APIs, task prices, and software-as-a-service protocols that allow employers / requesters to receive data produced by workers directly in to IT systems. As a result, many employers attempt to manage worker automatically through algorithms rather than responding to workers on a case-by-case basis or addressing their concerns. Responding to workers is difficult to scale to the employment levels enabled by human computation microwork platforms.<ref name="mw-cw">{{cite journal | last1 = Irani | first1 = Lilly|author1-link=Lilly Irani | year = 2015 | title = The Cultural Work of Microwork | journal = New Media & Society | volume = 17 | issue = 5 | pages = 720–739 | doi = 10.1177/1461444813511926| s2cid = 377594 }}</ref> Workers in the system Mechanical Turk, for example, have reported that human computation employers can be unresponsive to their concerns and needs<ref name="to-acm">{{cite journal | last1= Irani|first1=Lilly|last2 = Silberman | first2 = Six | year=2013|title=Turkopticon:Interrupting Workers Invisibility on Amazon Mechanical Turk|journal=Proceedings of SIGCHI 2013|series=Chi '13|pages=611–620|url=http://dl.acm.org/citation.cfm?id=2470742|doi=10.1145/2470654.2470742|isbn=9781450318990|s2cid=207203679}}</ref>
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