Panel 20

Organizers: Fabio Morreale (1); Elham Bahmanteymouri (1); Brent Burmester (1); Matteo Pasquinelli (2)

1: University of Auckland, New Zealand; 2: University of Arts and Design Karlsruhe, Germany

Topics: Working conditions and organizations interested in and by automation; Algorithmic knowledge, media ecologies and artificial intelligence; Ethics, innovation and responsibility in technoscience; Extractivist powers, imaginaries and asymmetries

Keywords: Labour exploitation; immaterial labour; AI-training; data extractivism; digital labour

An ever-growing number of digital and non-digital companies and governments embed forms of artificial intelligence (AI) in their technical infrastructure. The dominant AI technique, Machine Learning (ML), is based on the paradigm that computer systems can emulate humans when provided with enough “training data”. In most cases, this training data is the product of human labour, and the way in which it is collected is problematic. Data collection procedures are opaque; business models fail to account for the value of the labour being contributed by individuals, and consent to collect and use this data is not explicitly requested.

Particularly widespread are systems in which the training data is gathered simply by virtue of users voluntarily engaging with digital platforms and online tools for purposes other than contributing data to a training set used by AI systems. For example, an internet user filling out a reCAPTCHA is actually generating data that is then collected and used for various Google AI applications. As another example, Spotify’s music recommendations are informed by many different types of human input, including user interactions with the platforms (e.g. the music they like or skip, the playlists they create) and music reviews and comments written by music journalists and aficionados on blogs and forum that are scraped by Spotify bots to extract music taste automatically.

Individuals interacting with AI-powered systems are commonly unaware of the ongoing extraction of value as they volunteer their preferences, intelligence, and behaviours to AI owners. They are also commonly unaware of how their information and actions generate corporate profits. Using a Marxian lens, we frame these extractive practices as forms of labour and specifically immaterial labour that has an external value that individuals are steadily but inadvertently producing. Consequently, they cannot use the collective power this affords to make demands of their ‘employers’. The Marxian approach suggests a classification of knowledge class for all individuals whose interactions with AI generate value that is expropriated from them. Framing this issue using the theme of the conference, an interesting world to come would see new political struggles of the knowledge class whose work is exploited by digital capitalism and new ways to break the circuits of surplus value/surplus data as the engine of this type of capitalism.  

The topic of this panel is aligned with current STS discourses, including digital labour, AI ethics, and asymmetric power relations between digital corporations and their users. Given the highly interdisciplinary nature of this issue, we argue that STS is the perfect venue for these conversations to be finely clarified, confronted, addressed, and resolved. In this track, we encourage submissions across different domains discussing instances of labour exploitation and humanness extractivism in AI. Possible contributions include the following i) political and philosophical lenses to frame this phenomenon; ii) initiatives to uncover issues at the intersection of labour and AI; iii) methods to audit AI training sets; iv) proposition of possible forms of resistance; v) discussions of case studies to which this issue applies.