Views from Mechanical Turk

I asked the workers at Amazon Mechanical Turk to send me a photo of their view out of the window for $0.20. Here are the photos I received and the city they were sent from.

Context

The following texts are extracted and updated (June, 2022) from the research conducted as part of the individual thesis project in the master department Information Design at Design Academy Eindhoven (NL), 2019.
More information here.

Creating Datasets and Mechanical Turk

The ever-growing use of platforms such as Amazon Mechanical Turk, allow large groups of individuals to participate in creating, tagging and describing data.
Introduced by Amazon in 2005, Mechanical Turk is named after an 18th century “automatic” chess-playing machine, which was handily beating humans in chess games. Of course, the robot was not using any artificial intelligence algorithms back then. The secret of the Mechanical Turk machine was a human operator, hidden inside the machine, who was the real intelligence (Ipeirotis, 2010).
Today, Amazon Mechanical Turk is a marketplace for tasks that cannot be easily automated, which are then fulfilled by human users.
Such tasks, mainly involve the creation of datasets: collections of data that act as pedagogical learning material when dealing with Artificial intelligences, most specifically, machine learning systems. Current Artificial intelligences are, in fact, not programmed anymore in the old-fashioned way, where for instance to distinguish images of apples from pears lines of code expressing specific rules were written (e.g., if the colour is red, then it’s an apple). Instead, current machine learning models are able to "learn” to execute such tasks by simply repeatedly analysing large numbers of labelled examples (images of apples) and counterexamples (images of pears). Although these materials are often created to test the performance of algorithms, they tend to show how little attention is placed on the quality of the data itself.
Such data is analysed and processed by workers on platforms like Mechanical Turks. But little is known about these individuals that complete these tasks. Ipeirotis’s analysis shows that the majority of the workers are operating in the United States, with an increasing proportion coming from India in the past few years. But these analyses study the overall usage of those platforms, whereas there are no public traces of the turkers who helped shape any globally used dataset.

Images of the Process

Geography of Datasets

"Information has always had geography. It is from somewhere; about somewhere; it evolves and is transformed somewhere; it is mediaxted by networks, infrastructures, and technologies: all of which exist in physical, material places" (Graham et al., 2015).

Datasets are no exception. The information they carry is a reflection of time, politics and geographical contexts.
Although, in the past years, there has been a growing attention of bias studies in the field of machine learning, there is still a general disinterest for the ones carried by the hidden geographies of datasets. If in Code/Space, Robert Kitchin and Martin Dodge wrote: Software matters because it alters the conditions through which society, space, and time, and thus spatiality, are produced, we could rewrite the sentence by saying that data matters, because it alters the conditions through which society, space, and time, and thus spatiality, are produced.
Software produces multiple types of spatialities and datasets have their own geographic distribution. From the locations of their creators, that influence their browser navigation, to the digital platforms where the data is fetched, reaching until the original metadata of each source. All of these things are informational, but are also part of the place itself; they are part of how we enact and bring the place into being (Kitchin & Dodge, 2011).
The same research questions, should apply to the individuals that are employed to structure datasets.

The purpose of this website

As organisations use Mechanical Turk to create datasets, in this website a more private dataset is shown. Following the logic of the platform, for a small fee workers have been asked to show a glimpse of their private life, their view close to their desk, their working station. At the limit of ethics and labour exploitation we should ask ourselves whether this is an acceptable approach to use in research and in general, if employing randomic workers around the world is the ideal practice to shape future artificial intelligences.
With this experiment, design becomes a critical tool, it investigates the sources of datasets, and it helps framing a critical discussion around the decisions that few individuals are taking. As Farinelli calls for a focus on digital geographies, which still suffers from a lack of attention, the same applies for the need for an “algorithmic culture” (Dourish, 2016) (Gillespie, 2013)(Gillespie, T., & Seaver, N., 2015 )(Striphas, 2015). Or more specifically an algorithmic literacy (Gillespie, 2013), making people understand that the tools they engage with will give certain types of answers back, and if they find those politically troubling, they should see them as artefacts of the way those systems work (ibidem).
Design allows information to be questioned and awareness to be spread, even outside the strictly journalistic sense, focusing on what Verbeek (2005) calls mediated morality:

"Designers cannot but help to shape moral decisions and practices. Designing is materializing morality."

Bibliographic reference

Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2), 205395171666512. https://doi.org/10.1177/2053951716665128

Graham et al., 2015, Towards a study of information geographies: [im]mutable augmentations and a mapping of the geographies of information. Retrieved 17 January 2019, from https://rgs-ibg.onlinelibrary.wiley.com/doi/full/10.1002/geo2.8.

Gillespie, T. (2013, May 17). Tarleton Gillespie: ‘The Relevance of Algorithms’. Retrieved 30 January 2019, from https://vimeo.com/69641358

Gillespie, T., & Seaver, N., 2015, Critical Algorithm Studies: a Reading List. Retrieved 18 January 2019, from https://socialmediacollective.org/reading-lists/critical-algorithm-studies/

Ipeirotis, P. G. (2010). Analyzing the Amazon Mechanical Turk marketplace. XRDS: Crossroads, The ACM Magazine for Students, 17(2), 16–21. https://doi.org/10.1145/1869086.1869094

Kitchin, R., & Dodge, M. (2011). Code/space: software and everyday life. Cambridge, Mass: MIT Press.

Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4–5), 395–412. https://doi.org/10.1177/1367549415577392

Verbeek, P.-P. (2005). What things do: Philosophical reflections on technology, agency, and design. University Park, Pa: Pennsylvania State University Press.

Verbeek, P.P. (2011), Moralizing Technology: Understanding and Designing the Morality of Things. Chicago and London: University of Chicago Press.