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{{Short description|Computer science technique}}
'''Human-based computation''' ('''HBC'''), '''human-assisted computation''',<ref>{{cite web |author=Shahaf |
In traditional computation, a human employs a computer<ref>the term "computer" is used the modern usage of computer, not the one of [[human computer]]</ref> to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret.<ref>{{cite web|url=https://www.csee.umbc.edu/courses/471/papers/turing.pdf|author=Turing, Alan M.|title=Computer Machinery and Intelligence|date=1950|access-date=12 May 2022}}</ref> Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem,<ref>{{cite book|chapter-url=https://ieeexplore.ieee.org/document/1225961?section=abstract|chapter=Automatic concept evolution|author=Fogarty, Terence C.|title=The Second IEEE International Conference on Cognitive Informatics, 2003. Proceedings. |date=20 August 2003|page=89 |doi=10.1109/COGINF.2003.1225961 |isbn=0-7695-1986-5 |s2cid=30299981 |access-date=21 June 2021}}</ref> then collects, interprets, and integrates their solutions. This turns hybrid networks of humans and computers into "large scale distributed computing networks".<ref>{{Citation|last=von Ahn|first=Luis|title=Human Computation |date=22 August 2012 |url=https://www.youtube.com/watch?v=tx082gDwGcM |archive-url=https://ghostarchive.org/varchive/youtube/20211219/tx082gDwGcM |archive-date=2021-12-19 |url-status=live |volume=Google Tech Talk July 26, 2006 |access-date=2019-11-22}}{{cbignore}}. Cited after Mühlhoff, Rainer (2019). [https://journals.sagepub.com/doi/abs/10.1177/1461444819885334 "Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning".] New Media & Society: 146144481988533. doi:10.1177/1461444819885334. ISSN 1461-4448.</ref><ref>{{cite web |author=Gentry |
==Early work==
Human-based computation (apart from the [[Computer (job description)|historical meaning of "computer]]") research has its origins in the early work on [[interactive evolutionary computation]] (EC).<ref>{{cite book |url=https://link.springer.com/chapter/10.1007/3-540-61723-X_966 |author=Herdy, Michael |title=Evolution strategies with subjective selection. Basic Concepts of Evolutionary Computation. Volumen 1141, pp. 22-31 |date=1996|pages=22–31 |doi=10.1007/3-540-61723-X_966 |isbn=9783540706687 |access-date=12 May 2022}}</ref> The idea behind interactive evolutionary algorithms has been attributed to [[Richard Dawkins]]; in the Biomorphs software accompanying his book ''[[The Blind Watchmaker]]'' (Dawkins, 1986)<ref>{{cite web|url=https://archive.org/stream/BlindWatchmakerTheRichardDawkins/Blind_Watchmaker_The_-_Richard_Dawkins_djvu.txt|title=''The Blind Watchmaker'' |access-date=12 May 2022|author=Dawkins, Richard}}</ref> the preference of a human experimenter is used to guide the evolution of two-dimensional sets of line segments. In essence, this program asks a human to be the fitness function of an evolutionary algorithm, so that the algorithm can use human visual perception and aesthetic judgment to do something that a normal evolutionary algorithm cannot do. However, it is difficult to get enough evaluations from a single human if we want to evolve more complex shapes. [[Victor Johnston]]<ref>{{cite web |url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=5375195.PN.&OS=PN/5375195&RS=PN/5375195 |archive-url=https://archive.today/20131014033059/http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=/netahtml/PTO/srchnum.htm&r=1&f=G&l=50&s1=5375195.PN.&OS=PN/5375195&RS=PN/5375195 |url-status=dead |archive-date=October 14, 2013 |title=''Method and apparatus for generating composites of human faces'' |access-date=12 May 2022 |author=Johnston, Victor
A concept of the automatic [[Turing test]] pioneered by [[Moni Naor]] (1996)<ref>{{cite web |url=http://www.wisdom.weizmann.ac.il/~naor/PAPERS/human_abs.html |author=Naor, Moni |title=Verification of a human in the loop or Identification via the Turing Test |access-date=12 May 2021}}</ref> is another precursor of human-based computation. In Naor's test, the machine can control the access of humans and computers to a service by challenging them with a [[natural language processing]] (NLP) or [[computer vision]] (CV) problem to identify humans among them. The set of problems is chosen in a way that they have no algorithmic solution that is both effective and efficient at the moment. If it existed, such an algorithm could be easily performed by a computer, thus defeating the test. In fact, Moni Naor was modest by calling this an automated Turing test. The [[imitation game]] described by [[Alan Turing]] (1950) didn't propose using CV problems. It was only proposing a specific NLP task, while the Naor test identifies and explores a large [[AI-complete|class]] of problems, not necessarily from the ___domain of NLP, that could be used for the same purpose in both automated and non-automated versions of the test.
Finally, [[Human-based genetic algorithm]] (HBGA)<ref>{{cite book
==Classes of human-based computation==
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* (CH) [[Interactive genetic algorithm|Interactive EC]] (Dawkins, 1986; Caldwell and Johnston, 1991; Sims, 1991) IEC enables the user to create an abstract drawing only by selecting his/her favorite images, so the human only performs fitness computation and software performs the innovative role. [Unemi 1998] Simulated breeding style introduces no explicit fitness, just selection, which is easier for humans.<ref>{{cite journal |url=https://www.koreascience.or.kr/article/CFKO199811920543230.page |author=Unemi, Tastsuo |title=A Design of Multi-Field User Interface for Simulated Breeding, pp. 489-494 |journal=Proceedings of the Korean Institute of Intelligent Systems Conference |year=1998 |pages=489–494 |access-date=12 May 2022}}</ref>
* (HH<sub>2</sub>) [[Wiki]] ([[Ward Cunningham|Cunningham]], 1995) enabled editing the web content by multiple users, i.e. supported two types of human-based innovation (contributing new page and its incremental edits). However, the selection mechanism was absent until 2002, when wiki has been augmented with a revision history allowing for reversing of unhelpful changes. This provided means for selection among several versions of the same page and turned wiki into a [[The Wiki Way|tool]] supporting collaborative content evolution (would be classified as human-based evolution strategy in EC terms).
* (HH<sub>3</sub>) [[Human-based genetic algorithm]] (Kosorukoff, 1998) uses both human-based selection and three types of human-based innovation (contributing new content, mutation, and recombination). Thus, all operators of a typical [[genetic algorithm]] are outsourced to humans (hence the origin of '''human-based'''). This idea is extended to integrating crowds with genetic algorithm to study creativity in 2011.<ref>{{cite book |author=Yu |
* (HH<sub>1</sub>) [[Social search]] applications accept contributions from users and attempt to use human evaluation to select the fittest contributions that get to the top of the list. These use one type of human-based innovation. Early work was done in the context of HBGA. [[Digg]] and [[Reddit]] are recently popular examples. See also [[Collaborative filtering]].
* (HC) Computerized tests. A computer generates a problem and presents it to evaluate a user. For example, [[CAPTCHA]] tells human users from computer programs by presenting a problem that is supposedly easy for a human and difficult for a computer. While CAPTCHAs are effective security measures for preventing automated abuse of online services, the human effort spent solving them is otherwise wasted. The [[reCAPTCHA]] system makes use of these human cycles to help digitize books by presenting words from scanned old books that optical character recognition cannot decipher.<ref>{{cite web |author=von Ahn |
* (HC) Interactive online games: These are programs that extract knowledge from people in an entertaining way.<ref>{{cite web |url=http://www.20q.net/index.html |author=Burgener, Robin |title=20Q . net. Twenty Questions. The neural-net on the Internet. Play Twenty Questions |access-date=12 May 2022 |archive-url=https://web.archive.org/web/20000229091147/http://www.20q.net/index.html |archive-date=29 February 2000 |url-status=dead}}</ref><ref>{{cite web |author=von Ahn |
* (HC) "Human Swarming" or "Social Swarming". The UNU platform for human swarming establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified [[collective intelligence]].<ref>{{cite web |author=Rosenberg, Louis B. |title=Human Swarms: a real-time paradigm for Collective intelligence |url=http://sites.lsa.umich.edu/collectiveintelligence/wp-content/uploads/sites/176/2015/05/Rosenberg-CI-2015-Abstract.pdf |access-date=12 May 2021 |website=[[University of Michigan College of Literature, Science, and the Arts | University of Michigan College of LSA]]}}</ref><ref>{{cite web |url=https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |title=Swarms: a real-time paradigm for Collective intelligence |access-date=12 May 2022 |archive-url=https://web.archive.org/web/20151027132802/https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html |archive-date=27 October 2015 |url-status=dead}}</ref><ref>{{cite web |url=https://papers.ssrn.com/sol3/papers.cfm?abstract_id=924249 |title=Infotopia: How Many Minds Produce Knowledge |author=Sunstein, Cass R. |date=August 16, 2006 |ssrn=924249 |access-date=12 May 2022}}</ref><ref>{{cite journal |author=Malone |
* (NHC) Natural Human Computation involves leveraging existing human behavior to extract computationally significant work without disturbing that behavior.<ref>[https://arxiv.org/abs/1306.6376 Estrada, Daniel, and Jonathan Lawhead, "Gaming the Attention Economy" in ''The Springer Handbook of Human Computation'', Pietro Michelucci (ed.), (Springer, 2014)]</ref> NHC is distinguished from other forms of human-based computation in that rather than involving outsourcing computational work to human activity by asking humans to perform novel computational tasks, it involves taking advantage of previously unnoticed computational significance in existing behavior.
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==Human-based computation as a form of social organization==
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 |author=Kosorukoff |
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 (or more) 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 magazine |url=https://www.wired.com/2006/06/crowds/ |title=The Rise of Crowdsourcing |author=Howe, Jeff |magazine=Wired |date=June 2006|access-date=12 May 2022}}</ref> However, others have argued that crowdsourcing ought to be distinguished from true human-based computation.<ref>{{cite book |url=https://www.springer.com/gp/book/9781461488057 |title=Handbook of Human Computation |author=Michelucci, Pietro |access-date=12 May 2022}}</ref> Crowdsourcing does indeed involve the distribution of computation tasks across a number of human agents, but Michelucci argues that this is not sufficient for it to be considered human computation. Human computation requires not just that a task be distributed across different agents, but also that the set of agents across which the task is distributed be ''mixed:'' some of them must be humans, but others must be traditional computers. It is this mixture of different types of agents in a computational system that gives human-based computation its distinctive character. Some instances of crowdsourcing do indeed meet this criterion, but not all of them do.
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 book | last1= Irani|first1=Lilly|last2 = Silberman | first2 = Six |title=Proceedings of the SIGCHI Conference on Human Factors in Computing Systems |chapter=Turkopticon | year=2013|series=Chi '13|pages=611–620|chapter-url=http://dl.acm.org/citation.cfm?id=2470742|doi=10.1145/2470654.2470742|isbn=9781450318990|s2cid=207203679|url=https://escholarship.org/uc/item/10c125z3 }}</ref>
==Applications==
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