Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

Newlands, Gemma

doi:10.1177/20539517211016026

Cited by 35 publications

(26 citation statements)

References 88 publications

(110 reference statements)

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“…Sociologists of work, occupations, and organizations have examined how new algorithmic systems reconfigure human labor, in ways that are often detrimental to workers (Bailey et al., 2020; Griesbach et al., 2019; Kellogg et al., 2020; Newlands, 2021; Shestakofsky, 2017). Utopian and dystopian predictions of robots taking and transforming human jobs have been the subject of discourse analysis (see James & Whelan, 2021; Ossewaarde & Gulenc, 2020; Vicsek, 2020), but sociological scholarship has been skeptical or critical of these claims, and more attentive to questions of power relations (Boyd & Holton, 2018).…”

Section: Social Inequality and Technology: The View From Sociologymentioning

confidence: 99%

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

Zajko

2022

Sociology Compass

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Artificial intelligence (AI) and algorithmic systems have been criticized for perpetuating bias, unjust discrimination, and contributing to inequality. Artificial intelligence researchers have remained largely oblivious to existing scholarship on social inequality, but a growing number of sociologists are now addressing the social transformations brought about by AI. Where bias is typically presented as an undesirable characteristic that can be removed from AI systems, engaging with social inequality scholarship leads us to consider how these technologies reproduce existing hierarchies and the positive visions we can work towards. I argue that sociologists can help assert agency over new technologies through three kinds of actions: (1) critique and the politics of refusal;(2) fighting inequality through technology; and (3) governance of algorithms. As we become increasingly dependent on AI and automated systems, the dangers of further entrenching or amplifying social inequalities have been well documented, particularly with the growing adoption of these systems by government agencies. However, public policy also presents some opportunities to restructure social dynamics in a positive direction, as long as we can articulate what we are trying to achieve, and are aware of the risks and

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Section: Social Inequality and Technology: The View From Sociologymentioning

confidence: 99%

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

Zajko

2022

Sociology Compass

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“…AI verification involves the evaluation of algorithmic outputs. Finally, AI impersonation, often seen in the corporate and AI-asa-service sector [59], refers to the non-disclosed "'human-in-the-loop' principle that makes workers hardly distinguishable from algorithms" [81].…”

Section: Different Tasks Different Instructionsmentioning

confidence: 99%

The Data-Production Dispositif

Miceli¹,

Posada²

2022

Preprint

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Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platforms. This paper investigates outsourced ML data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor. Our dispositif analysis comprises the examination of 210 data work instruction documents, 55 interviews with data workers, managers, and requesters, and participant observation. Our findings show that discourses encoded in instructions reproduce and normalize the worldviews of requesters. Precarious working conditions and economic dependency alienate workers, making them obedient to instructions. Furthermore, discourses and social contexts materialize in artifacts, such as interfaces and performance metrics, limiting workers' agency and normalizing specific ways of interpreting data. We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.

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“…But in general, the presence of humans remains in the backstage, in order not to diminish the appeal of automation on which technology companies base their marketing. According to Newlands (2021), the curtain on these human contributions is only lifted when vendors must co-opt unpaid labour from users, for example to train localised chatbot services, and need to elicit their cooperation.…”

Section: Who Learns In Machine Learning? the Role Of Humansmentioning

confidence: 99%

Learners in the loop: hidden human skills in machine intelligence

Tubaro¹

2022

Sociologia Del Lavoro

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Today's artificial intelligence, largely based on data-intensive machine learning algorithms, relies heavily on the digital labour of invisibilized and precarized humans-in-the-loop who perform multiple functions of data preparation, verification of results, and even impersonation when algorithms fail. Using original quantitative and qualitative data, the present article shows that these workers are highly educated, engage significant (sometimes advanced) skills in their activity, and earnestly learn alongside machines. However, the loop is one in which human workers are at a disadvantage as they experience systematic misrecognition of the value of their competencies and of their contributions to technology, the economy, and ultimately society. This situation hinders negotiations with companies, shifts power away from workers, and challenges the traditional balancing role of the salary institution.

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Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

Cited by 35 publications

References 88 publications

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates

The Data-Production Dispositif

Learners in the loop: hidden human skills in machine intelligence

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