Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking (by analogy to distributed computing) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans, usually as microwork. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training deep learning-based Artificial Intelligence systems. In this case, human-based computation has been referred to as human-aided artificial intelligence. In traditional computation, a human employs a computer to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions. This turns hybrid networks of humans and computers into "large scale distributed computing networks". where code is partially executed in human brains and on silicon based processors.
Early work Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation (EC). The idea behind interactive evolutionary algorithms has been attributed to Richard Dawkins; in the Biomorphs software accompanying his book The Blind Watchmaker (Dawkins, 1986) 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 and Karl Sims extended this concept by harnessing the power of many people for fitness evaluation (Caldwell and Johnston, 1991; Sims, 1991). As a result, their programs could evolve beautiful faces and pieces of art appealing to the public. These programs effectively reversed the common interaction between computers and humans. In these programs, the computer is no longer an agent of its user, but instead, a coordinator aggregating efforts of many human evaluators. These and other similar research efforts became the topic of research in aesthetic selection or interactive evolutionary computation (Takagi, 2001), however the scope of this research was limited to outsourcing evaluation and, as a result, it was not fully exploring the full potential of the outsourcing. A concept of the automatic Turing test pioneered by Moni Naor (1996) 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 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) encourages human participation in multiple different roles. Humans are not limited to the role of evaluator or some other predefined role, but can choose to perform a more diverse set of tasks. In particular, they can contribute their innovative solutions into the evolutionary process, make incremental changes to existing solutions, and perform intelligent recombination. In short, HBGA allows humans to participate in all operations of a typical genetic algorithm. As a result of this, HBGA can process solutions for which there are no computational innovation operators available, for example, natural languages. Thus, HBGA obviated the need for a fixed representational scheme that was a limiting factor of both standard and interactive EC. These algorithms can also be viewed as novel forms of social organization coordinated by a computer, according to Alex Kosorukoff and David Goldberg.
Classes of human-based computation Human-based computation methods combine computers and humans in different roles. Kosorukoff (2000) proposed a way to describe division of labor in computation, that groups human-based methods into three classes. The following table uses the evolutionary computation model to describe four classes of computation, three of which rely on humans in some role. For each class, a representative example is shown. The classification is in terms of the roles (innovation or selection) performed in each case by humans and computational processes. This table is a slice of a three-dimensional table. The third dimension defines if the organizational function is performed by humans or a computer. Here it is assumed to be performed by a computer.
Classes of human-based computation from this table can be referred by two-letter abbreviations: HC, CH, HH. Here the first letter identifies the type of agents performing innovation, the second letter specifies the type of selection agents. In some implementations (wiki is the most common example), human-based selection functionality might be limited, it can be shown with small h.
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