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71-443: The service was conceived by Venky Harinarayan in a U.S. patent disclosure in 2001. Amazon coined the term artificial artificial intelligence for processes that outsource some parts of a computer program to humans, for those tasks carried out much faster by humans than computers. It is claimed that Jeff Bezos was responsible for proposing the development of Amazon's Mechanical Turk to realize this process. The name Mechanical Turk

142-412: A regularization penalty into the optimization. The regularization penalty can be viewed as implementing a form of Occam's razor that prefers simpler functions over more complex ones. A wide variety of penalties have been employed that correspond to different definitions of complexity. For example, consider the case where the function g {\displaystyle g} is a linear function of

213-480: A Bayesian interpretation as the negative log prior probability of g {\displaystyle g} , − log ⁡ P ( g ) {\displaystyle -\log P(g)} , in which case J ( g ) {\displaystyle J(g)} is the posterior probability of g {\displaystyle g} . The training methods described above are discriminative training methods, because they seek to find

284-511: A Worker's U.S. bank account. Requesters can ask that Workers fulfill qualifications before engaging in a task, and they can establish a test designed to verify the qualification. They can also accept or reject the result sent by the Worker, which affects the Worker's reputation. As of April 2019, Requesters paid Amazon a minimum 20% commission on the price of successfully completed jobs, with increased amounts for additional services . Requesters can use

355-533: A bias/variance parameter that the user can adjust). The second issue is of the amount of training data available relative to the complexity of the "true" function (classifier or regression function). If the true function is simple, then an "inflexible" learning algorithm with high bias and low variance will be able to learn it from a small amount of data. But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of

426-729: A computer interface to help employers perform tasks that are not possible using a true machine. MTurk launched publicly on November 2, 2005. Its user base grew quickly. In early- to mid-November 2005, there were tens of thousands of jobs, all uploaded to the system by Amazon itself for some of its internal tasks that required human intelligence. HIT types expanded to include transcribing, rating, image tagging, surveys, and writing. In March 2007, there were reportedly more than 100,000 workers in over 100 countries. This increased to over 500,000 registered workers from over 190 countries in January 2011. That year, Techlist published an interactive map pinpointing

497-497: A crash site or other evidence that should be examined more closely. This search was also unsuccessful. The satellite imagery was mostly within a 50-mile radius, but the crash site was eventually found by hikers about a year later, 65 miles away. MTurk has also been used as a tool for artistic creation. One of the first artists to work with Mechanical Turk was xtine burrough , with The Mechanical Olympics (2008), Endless Om (2015), and Mediations on Digital Labor (2015). Another work

568-564: A function g {\displaystyle g} that discriminates well between the different output values (see discriminative model ). For the special case where f ( x , y ) = P ( x , y ) {\displaystyle f(x,y)=P(x,y)} is a joint probability distribution and the loss function is the negative log likelihood − ∑ i log ⁡ P ( x i , y i ) , {\displaystyle -\sum _{i}\log P(x_{i},y_{i}),}

639-489: A lack of power when disputes occur. Journalist Alana Semuels’s article "The Internet Is Enabling a New Kind of Poorly Paid Hell" in The Atlantic is typical of such criticisms of MTurk. Some academic papers have obtained findings that support or serve as the basis for such common criticisms, but others contradict them. A recent academic commentary argued that study participants on sites like MTurk should be clearly warned about

710-632: A large scale, which would be difficult outside of a crowd platform. Mechanical Turk allows Requesters to amass a large number of responses to various types of surveys, from basic demographics to academic research. Other uses include writing comments, descriptions, and blog entries to websites and searching data elements or specific fields in large government and legal documents. Companies use Mechanical Turk's crowd labor to understand and respond to different types of data. Common uses include editing and transcription of podcasts, translation, and matching search engine results. The validity of research conducted with

781-524: A representative sample of the general population. Instead, MTurk is a nonprobability, convenience sample. Descriptive research is best conducted with a probability-based, representative sample of the population researchers want to understand. When compared to the general population, people on MTurk are younger, more highly educated, more liberal, and less religious. Mechanical Turk has been criticized by journalists and activists for its interactions with and use of labor. Computer scientist Jaron Lanier noted how

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852-482: A risk minimization algorithm is said to perform generative training , because f {\displaystyle f} can be regarded as a generative model that explains how the data were generated. Generative training algorithms are often simpler and more computationally efficient than discriminative training algorithms. In some cases, the solution can be computed in closed form as in naive Bayes and linear discriminant analysis . There are several ways in which

923-538: A small minority of their HITs rejected, perhaps as low as 1%. In the Facebook–Cambridge Analytica data scandal , Mechanical Turk was one of the means of covertly gathering private information for a massive database. The system paid people a dollar or two to install a Facebook -connected app and answer personal questions. The survey task, as a work for hire, was not used for a demographic or psychological research project as it might have seemed. The purpose

994-455: A special partner to NeoTribe Ventures . Supervised Machine Learning In machine learning , supervised learning ( SL ) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a supervisory signal ), which are often human-made labels. The training process builds a function that maps new data to expected output values. An optimal scenario will allow for

1065-824: A task is one cent. Because tasks are typically simple and repetitive the majority of tasks pay only a few cents, but there are also well-paying tasks on the site. Many criticisms of MTurk stem from the fact that a majority of tasks offer low wages. In addition, workers are considered independent contractors rather than employees. Independent contractors are not protected by the Fair Labor Standards Act or other legislation that protects workers’ rights. Workers on MTurk must compete with others for good HIT opportunities as well as spend time searching for tasks and other actions that they are not compensated for. The low payment offered for many tasks has fueled criticism of Mechanical Turk for exploiting and not compensating workers for

1136-443: Is a common practice among "gig economy" platforms. Workers are legally required to report their income as self-employment income. In 2013, the average wage for the multiple microtasks assigned, if performed quickly, was about one dollar an hour, with each task averaging a few cents. However, calculating people's average hourly earnings on a microtask site is extremely difficult and several sources of data show average hourly earnings in

1207-404: Is a conditional probability distribution P ( y | x ) {\displaystyle P(y|x)} and the loss function is the negative log likelihood: L ( y , y ^ ) = − log ⁡ P ( y | x ) {\displaystyle L(y,{\hat {y}})=-\log P(y|x)} , then empirical risk minimization

1278-445: Is a conditional probability model. There are two basic approaches to choosing f {\displaystyle f} or g {\displaystyle g} : empirical risk minimization and structural risk minimization . Empirical risk minimization seeks the function that best fits the training data. Structural risk minimization includes a penalty function that controls the bias/variance tradeoff. In both cases, it

1349-537: Is accessible via API from the following languages: Python, JavaScript, Java, .NET, Go, Ruby, PHP, or C++. Web sites and web services can use the API to integrate MTurk work into other web applications, providing users with alternatives to the interface Amazon has built for these functions. Amazon Mechanical Turk provides a platform for processing images, a task well-suited to human intelligence. Requesters have created tasks that ask workers to label objects found in an image, select

1420-552: Is among the top 600 most cited computer science articles over the last 20 years. Together with four other engineers, Harinarayan founded Junglee Corp. in 1996. Junglee Corp. pioneered Internet comparison shopping. Junglee Corp. was acquired by Amazon.com Inc. in August 1998 for 1.6 million shares of stock valued at $ 250 million. Harinarayan then became general manager at Amazon.com, where he worked with founder and CEO Jeff Bezos to help create Amazon.com's marketplace business. Marketplace

1491-445: Is assumed that the training set consists of a sample of independent and identically distributed pairs , ( x i , y i ) {\displaystyle (x_{i},\;y_{i})} . In order to measure how well a function fits the training data, a loss function L : Y × Y → R ≥ 0 {\displaystyle L:Y\times Y\to \mathbb {R} ^{\geq 0}}

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1562-460: Is because the many "extra" dimensions can confuse the learning algorithm and cause it to have high variance. Hence, input data of large dimensions typically requires tuning the classifier to have low variance and high bias. In practice, if the engineer can manually remove irrelevant features from the input data, it will likely improve the accuracy of the learned function. In addition, there are many algorithms for feature selection that seek to identify

1633-425: Is better suited for some types of research than others. MTurk appears well-suited for questions that seek to understand whether two or more things are related to each other (called correlational research; e.g., are happy people more healthy?) and questions that attempt to show one thing causes another thing (experimental research; e.g., being happy makes people more healthy). Fortunately, these categories capture most of

1704-488: Is comparable in some respects to the now discontinued Google Answers service. However, the Mechanical Turk is a more general marketplace that can potentially help distribute any kind of work tasks all over the world. The Collaborative Human Interpreter (CHI) by Philipp Lenssen also suggested using distributed human intelligence to help computer programs perform tasks that computers cannot do well. MTurk could be used as

1775-400: Is considerably lower than many other means of conducting surveys, so many researchers continue to use it. The general consensus among researchers is that the service works best for recruiting a diverse sample; it is less successful with studies that require more precisely defined populations or that require a representative sample of the population as a whole. Many papers have been published on

1846-534: Is currently Amazon.com's most profitable and fastest-growing business, accounting for almost 25% of all US transactions. Harinarayan also was an inventor of the concept underlying Amazon.com's Mechanical Turk . Harinarayan and his business partner, Anand Rajaraman, co-founded Cambrian Ventures, an early stage venture capital fund, in 2000. Cambrian went on to back several companies later acquired by Google . Cambrian funded companies like Mobissimo , Aster Data Systems and TheFind.com . In 2017, Harinarayan became

1917-626: Is defined as returning the y {\displaystyle y} value that gives the highest score: g ( x ) = arg ⁡ max y f ( x , y ) {\displaystyle g(x)={\underset {y}{\arg \max }}\;f(x,y)} . Let F {\displaystyle F} denote the space of scoring functions. Although G {\displaystyle G} and F {\displaystyle F} can be any space of functions, many learning algorithms are probabilistic models where g {\displaystyle g} takes

1988-540: Is defined as the expected loss of g {\displaystyle g} . This can be estimated from the training data as In empirical risk minimization, the supervised learning algorithm seeks the function g {\displaystyle g} that minimizes R ( g ) {\displaystyle R(g)} . Hence, a supervised learning algorithm can be constructed by applying an optimization algorithm to find g {\displaystyle g} . When g {\displaystyle g}

2059-512: Is defined. For training example ( x i , y i ) {\displaystyle (x_{i},\;y_{i})} , the loss of predicting the value y ^ {\displaystyle {\hat {y}}} is L ( y i , y ^ ) {\displaystyle L(y_{i},{\hat {y}})} . The risk R ( g ) {\displaystyle R(g)} of function g {\displaystyle g}

2130-444: Is equivalent to maximum likelihood estimation . When G {\displaystyle G} contains many candidate functions or the training set is not sufficiently large, empirical risk minimization leads to high variance and poor generalization. The learning algorithm is able to memorize the training examples without generalizing well (overfitting). Structural risk minimization seeks to prevent overfitting by incorporating

2201-528: Is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem ). There are four major issues to consider in supervised learning: A first issue is the tradeoff between bias and variance . Imagine that we have available several different, but equally good, training data sets. A learning algorithm is biased for a particular input x {\displaystyle x} if, when trained on each of these data sets, it

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2272-405: Is present, it is better to go with a higher bias, lower variance estimator. In practice, there are several approaches to alleviate noise in the output values such as early stopping to prevent overfitting as well as detecting and removing the noisy training examples prior to training the supervised learning algorithm. There are several algorithms that identify noisy training examples and removing

2343-400: Is systematically incorrect when predicting the correct output for x {\displaystyle x} . A learning algorithm has high variance for a particular input x {\displaystyle x} if it predicts different output values when trained on different training sets. The prediction error of a learned classifier is related to the sum of the bias and the variance of

2414-415: Is the feature vector of the i {\displaystyle i} -th example and y i {\displaystyle y_{i}} is its label (i.e., class), a learning algorithm seeks a function g : X → Y {\displaystyle g:X\to Y} , where X {\displaystyle X} is the input space and Y {\displaystyle Y}

2485-498: Is the output space. The function g {\displaystyle g} is an element of some space of possible functions G {\displaystyle G} , usually called the hypothesis space . It is sometimes convenient to represent g {\displaystyle g} using a scoring function f : X × Y → R {\displaystyle f:X\times Y\to \mathbb {R} } such that g {\displaystyle g}

2556-500: The L 0 {\displaystyle L_{0}} "norm" , which is the number of non-zero β j {\displaystyle \beta _{j}} s. The penalty will be denoted by C ( g ) {\displaystyle C(g)} . The supervised learning optimization problem is to find the function g {\displaystyle g} that minimizes The parameter λ {\displaystyle \lambda } controls

2627-509: The Farallon Islands on Mechanical Turk. A front-page story on Digg attracted 12,000 searchers who worked with imaging professionals on the same data. The search was unsuccessful. In September 2007, a similar arrangement was repeated in the search for aviator Steve Fossett . Satellite data was divided into 85-square-metre (910 sq ft) sections, and Mechanical Turk users were asked to flag images with "foreign objects" that might be

2698-443: The design of Mechanical Turk "allows you to think of the people as software components" in a way that conjures "a sense of magic, as if you can just pluck results out of the cloud at an incredibly low cost". A similar point is made in the book Ghostwork by Mary L. Gray and Siddharth Suri. Critics of MTurk argue that workers are forced onto the site by precarious economic conditions and then exploited by requesters with low wages and

2769-418: The $ 5–$ 9 per hour range among a substantial number of Workers, while the most experienced, active, and proficient workers may earn over $ 20 per hour. Workers can have a postal address anywhere in the world. Payment for completing tasks can be redeemed on Amazon.com via gift certificate (gift certificates are the only payment option available to international workers, apart from India) or can be transferred to

2840-547: The Amazon Mechanical Turk API to programmatically integrate the results of the work directly into their business processes and systems. When employers set up a job, they must specify as well as the specific details about the job they want to be completed. Workers have been primarily located in the United States since the platform's inception with demographics generally similar to the overall Internet population in

2911-486: The CloudResearch study found average wages of about $ 6.61 per hour. Some evidence suggests that very active and experienced people can earn $ 20 per hour or more. The Nation magazine reported in 2014 that some Requesters had taken advantage of Workers by having them do the tasks, then rejecting their submissions in order to avoid paying them. Available data indicates that rejections are fairly rare. Workers report having

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2982-633: The Guidelines for Academic Requesters and the Dear Jeff Bezos Campaign. Amazon made it harder for workers to enroll in Dynamo by closing the request account that provided workers with a required code for Dynamo membership. Workers created third-party plugins to identify higher paying tasks, but Amazon updated its website to prevent these plugins from working. Workers have complained that Amazon's payment system will on occasion stop working. Mechanical Turk

3053-413: The Mechanical Turk worker pool has long been debated among experts. This is largely because questions of validity are complex: they involve not only questions of whether the research methods were appropriate and whether the study was well-executed, but also questions about the goal of the project, how the researchers used MTurk, who was sampled, and what conclusions were drawn. Most experts agree that MTurk

3124-465: The U.S. Within the U.S. workers are fairly evenly spread across states, proportional to each state’s share of the U.S. population. As of 2019, between 15 and 30 thousand people in the U.S. complete at least one HIT each month and about 4,500 new people join MTurk each month. Cash payments for Indian workers were introduced in 2010, which updated the demographics of workers, who however remained primarily within

3195-595: The United States. A website showing worker demographics in May 2015 showed that 80% of workers were located in the United States, with the remaining 20% located elsewhere in the world, most of whom were in India. In May 2019, approximately 60% were in the U.S., 40% elsewhere (approximately 30% in India). In early 2023 about 90% of workers were from the U.S. and about half of the remainder from India. Since 2010, numerous researchers have explored

3266-501: The algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias ). This statistical quality of an algorithm is measured via a generalization error . To solve a given problem of supervised learning, the following steps must be performed: A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There

3337-469: The bias-variance tradeoff. When λ = 0 {\displaystyle \lambda =0} , this gives empirical risk minimization with low bias and high variance. When λ {\displaystyle \lambda } is large, the learning algorithm will have high bias and low variance. The value of λ {\displaystyle \lambda } can be chosen empirically via cross-validation . The complexity penalty has

3408-417: The circumstances in which they might later be denied payment as a matter of ethics, even though such statements may not reduce the rate of careless responding. A paper published by a team at CloudResearch shows that only about 7% of people on MTurk view completing HITs as something akin to a full-time job. Most people report that MTurk is a way to earn money during their leisure time or as a side gig. In 2019,

3479-435: The demographics of the MTurk population. MTurk workers tend to be younger, more educated, more liberal, and slightly less wealthy than the U.S. population overall. Supervised Machine Learning algorithms require large amounts of human-annotated data to be trained successfully. Machine learning researchers have hired Workers through Mechanical Turk to produce datasets such as SQuAD, a question answering dataset. Since 2007,

3550-498: The execution engine for the CHI. In 2014 the Russian search giant Yandex launched a similar system called Toloka that is similar to the Mechanical Turk. Venky Harinarayan Venkatesh "Venky" Harinarayan is an Indian entrepreneur . He is the co-founder of Cambrian Ventures and Kosmix . Harinarayan also co-founded Junglee Corp . and played a significant role at Amazon.com in

3621-541: The form A popular regularization penalty is ∑ j β j 2 {\displaystyle \sum _{j}\beta _{j}^{2}} , which is the squared Euclidean norm of the weights, also known as the L 2 {\displaystyle L_{2}} norm. Other norms include the L 1 {\displaystyle L_{1}} norm, ∑ j | β j | {\displaystyle \sum _{j}|\beta _{j}|} , and

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3692-566: The form of a conditional probability model g ( x ) = arg ⁡ max y P ( y | x ) {\displaystyle g(x)={\underset {y}{\arg \max }}\;P(y|x)} , or f {\displaystyle f} takes the form of a joint probability model f ( x , y ) = P ( x , y ) {\displaystyle f(x,y)=P(x,y)} . For example, naive Bayes and linear discriminant analysis are joint probability models, whereas logistic regression

3763-406: The input space), then the function will only be able to learn with a large amount of training data paired with a "flexible" learning algorithm with low bias and high variance. A third issue is the dimensionality of the input space. If the input feature vectors have large dimensions, learning the function can be difficult even if the true function only depends on a small number of those features. This

3834-479: The late 1990s. Originally from Bombay , India , Harinarayan has a PhD in computer science from Stanford University (1997, under Jeffrey Ullman ) and a masters from UCLA. Bachelor of Technology He completed his bachelor's degree in Computer Science from IIT Madras (Class of 1988). While at Stanford, Harinarayan co-wrote a paper on implementing data cubes with Anand Rajaraman and Jeff Ullman , which

3905-515: The learning algorithm should not attempt to find a function that exactly matches the training examples. Attempting to fit the data too carefully leads to overfitting . You can overfit even when there are no measurement errors (stochastic noise) if the function you are trying to learn is too complex for your learning model. In such a situation, the part of the target function that cannot be modeled "corrupts" your training data - this phenomenon has been called deterministic noise . When either type of noise

3976-454: The learning algorithm. Generally, there is a tradeoff between bias and variance. A learning algorithm with low bias must be "flexible" so that it can fit the data well. But if the learning algorithm is too flexible, it will fit each training data set differently, and hence have high variance. A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing

4047-737: The locations of 50,000 of their MTurk workers around the world. By 2018, research demonstrated that while over 100,000 workers were available on the platform at any time, only around 2,000 were actively working. A user of Mechanical Turk can be either a "Worker" (contractor) or a "Requester" (employer). Workers have access to a dashboard that displays three sections: total earnings, HIT status, and HIT totals. Workers set their own hours and are not under any obligation to accept any particular task. Amazon classifies Workers as contractors rather than employees and does not pay payroll taxes. Classifying Workers as contractors allows Amazon to avoid things like minimum wage , overtime , and workers compensation —this

4118-605: The most relevant picture in a group of pictures, screen inappropriate content, classify objects in satellite images, or digitize text from images such as scanned forms filled out by hand. Companies with large online catalogues use Mechanical Turk to identify duplicates and verify details of item entries. For example: removing duplicates in yellow pages directory listings, checking restaurant details (e.g. phone number and hours), and finding contact information from web pages (e.g. author name and email). Diversification and scale of personnel of Mechanical Turk allow collecting information at

4189-680: The performance of a learning algorithm can be very time-consuming. Given fixed resources, it is often better to spend more time collecting additional training data and more informative features than it is to spend extra time tuning the learning algorithms. The most widely used learning algorithms are: Given a set of N {\displaystyle N} training examples of the form { ( x 1 , y 1 ) , . . . , ( x N , y N ) } {\displaystyle \{(x_{1},y_{1}),...,(x_{N},\;y_{N})\}} such that x i {\displaystyle x_{i}}

4260-455: The relevant features and discard the irrelevant ones. This is an instance of the more general strategy of dimensionality reduction , which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm. A fourth issue is the degree of noise in the desired output values (the supervisory target variables ). If the desired output values are often incorrect (because of human error or sensor errors), then

4331-443: The research conducted by behavioral scientists, and most correlational and experimental findings found in nationally representative samples replicate on MTurk. The type of research that is not well-suited for MTurk is often called "descriptive research." Descriptive research seeks to describe how or what people think, feel, or do; one example is public opinion polling. MTurk is not well-suited to such research because it does not select

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4402-494: The resultant collected data could be worthless. Accounts using so-called automated bots have been banned. There are services that extend the capabilities to MTurk. Amazon makes available an application programming interface (API) for the MTurk system. The MTurk API lets a programmer submit jobs, retrieve completed work, and approve or reject that work. In 2017, Amazon launched support for AWS Software Development Kits (SDK), allowing for nine new SDKs available to MTurk Users. MTurk

4473-449: The service has been used to search for prominent missing individuals. This use was first suggested during the search for James Kim , but his body was found before any technical progress was made. That summer, computer scientist Jim Gray disappeared on his yacht and Amazon's Werner Vogels , a personal friend, made arrangements for DigitalGlobe , which provides satellite data for Google Maps and Google Earth , to put recent photography of

4544-425: The suspected noisy training examples prior to training has decreased generalization error with statistical significance . Other factors to consider when choosing and applying a learning algorithm include the following: When considering a new application, the engineer can compare multiple learning algorithms and experimentally determine which one works best on the problem at hand (see cross-validation ). Tuning

4615-527: The true value of the task they complete. One study of 3.8 million tasks completed by 2,767 workers showed that "workers earned a median hourly wage of about $ 2 an hour" with 4% of workers earning more than $ 7.25 per hour. The Pew Research Center and the International Labour Office published data indicating people made around $ 5.00 per hour in 2015. A study focused on workers in the U.S. indicated average wages of at least $ 5.70 an hour, and data from

4686-463: The types of quality control approaches used by researchers (such as checking for bots, VPN users, or workers willing to submit dishonest responses) can meaningfully influence survey results. They demonstrated this via impact on three common behavioral/mental healthcare screening tools. Even though managing data quality requires work from researchers, there is a large body of research showing how to gather high quality data from MTurk. The cost of using MTurk

4757-477: The typical worker spent five to eight hours per week and earned around $ 7 per hour. The sampled workers did not report rampant mistreatment at the hands of requesters; they reported trusting requesters more than employers outside of MTurk. Similar findings were presented in a review of MTurk by the Fair Crowd Work organization, a collective of crowd workers and unions. The minimum payment that Amazon allows for

4828-601: The viability of Mechanical Turk to recruit subjects for social science experiments. Researchers have generally found that while samples of respondents obtained through Mechanical Turk do not perfectly match all relevant characteristics of the U.S. population, they are also not wildly misrepresentative. As a result, thousands of papers that rely on data collected from Mechanical Turk workers are published each year, including hundreds in top ranked academic journals. A challenge with using MTurk for human-subject research has been maintaining data quality. A study published in 2021 found that

4899-420: Was artist Aaron Koblin 's Ten Thousand Cents (2008). Programmers have developed browser extensions and scripts designed to simplify the process of completing jobs. Amazon has stated that they disapprove of scripts that completely automate the process and preclude the human element. This is because of the concern that the task completion process—e.g. answering a survey—could be gamed with random responses, and

4970-502: Was inspired by " The Turk ", an 18th-century chess-playing automaton made by Wolfgang von Kempelen that toured Europe, and beat both Napoleon Bonaparte and Benjamin Franklin . It was later revealed that this "machine" was not an automaton, but a human chess master hidden in the cabinet beneath the board and controlling the movements of a humanoid dummy. Analogously, the Mechanical Turk online service uses remote human labor hidden behind

5041-673: Was instead to bait the worker to reveal personal information about the worker's identity that was not already collected by Facebook or Mechanical Turk. Others have criticized that the marketplace does not allow workers to negotiate with employers. In response to criticisms of payment evasion and lack of representation, a group developed a third-party platform called Turkopticon which allows workers to give feedback on their employers. This allows workers to avoid potentially unscrupulous jobs and to recommend superior employers. Another platform called Dynamo allows workers to collect anonymously and organize campaigns to better their work environment, such as

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