"The Nooscope manifested: AI as instrument of knowledge extractivism"
by Pasquinelli, Matteo; Joler, Vladan (2020)
Abstract
Some enlightenment regarding the project to mechanise reason. The assembly line of machine learning: data, algorithm, model. The training dataset: the social origins of machine intelligence. The history of AI as the automation of perception. The learning algorithm: compressing the world into a statistical model. All models are wrong, but some are useful. World to vector: the society of classification and prediction bots. Faults of a statistical instrument: the undetection of the new. Adversarial intelligence vs. statistical intelligence: labour in the age of AI.
Key Passage
The natures of the ‘input’ and ‘output’ of machine learning have to be clarified. AI troubles are not only about information bias but also labour. AI is not just a control apparatus, but also a productive one. As just mentioned, an invisible workforce is involved in each step of its assembly line (dataset composition, algorithm supervision, model evaluation, etc.). Pipelines of endless tasks innervate from the Global North into the Global South; crowdsourced platforms of workers from Venezuela, Brazil and Italy, for instance, are crucial to teach German self-driving cars ‘how to see’. Against the idea of alien intelligence at work, it must be stressed that in the whole computing process of AI the human worker has never left the loop, or put more accurately, has never left the assembly line. Mary Gray and Siddharth Suri coined the term ‘ghost work’ for the invisible labour that makes AI appear artificially autonomous. (p.16)
Keywords
Ethical Machine Learning, Information Compression, Mechanised Knowledge, Nooscope, Political EconomyThemes
Digital Labour, AutomationLinks to Reference
- http://dx.doi.org/10.1007/s00146-020-01097-6
- https://www.ncbi.nlm.nih.gov/pubmed/33250587
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680082
- https://link.springer.com/article/10.1007/s00146-020-01097-6
Citation
Share
How to contribute.
