Learners in the loop
Hidden human skills in machine intelligence
Bibliographic Data
| ID | 21545176 |
|---|---|
| Authors | P Tubaro (0000-0002-1215-9145, Biologie Computationnelle, Quantitative et Synthétique, corresponding author) |
| Year | 2022 |
| Issue | 163 |
| Pages | 110-129 |
| Publication date | 2022-08-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Sociologia del Lavoro (JOURNAL) |
| Journal identifiers | ISSN: 0392-5048 • E-ISSN: 1972-554X |
| Publisher | Franco Angeli (PUBLISHER) |
| DOI | 10.3280/sl2022-163006 |
| OpenAlex | W4297070767 |
| Language | EN |
| Citations received | 3 |
| References cited | 27 |
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
Data science · Disadvantage · Institution · Knowledge management · Machine learning · Negotiation · Political science · Qualitative property · Salary · Social science · Sociology · Computer Science · Digital Economy and Work Transformation · Ethics and Social Impacts of AI · Law · Mathematics · Artificial Intelligence
Relational Inequalities
Demographics and Dynamics of Mechanical Turk Workers
Skills‐based Full Employment
Beyond the ‘salary institution’
Counting ‘micro-workers’
Embedded reproduction in platform data work
Heteromation, and Other Stories of Computing and Capitalism
The global gig economy
Machine learning in tutorials - Universal applicability, underinformed application, and other misconceptions
On the genealogy of machine learning datasets
| Citations per year | 1 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |