The Impossibility of Automating Ambiguity.
Complexity
artificial intelligence
embodiment
equity
machine learning
racial justice
Journal
Artificial life
ISSN: 1530-9185
Titre abrégé: Artif Life
Pays: United States
ID NLM: 9433814
Informations de publication
Date de publication:
11 06 2021
11 06 2021
Historique:
entrez:
16
9
2021
pubmed:
17
9
2021
medline:
29
3
2022
Statut:
ppublish
Résumé
On the one hand, complexity science and enactive and embodied cognitive science approaches emphasize that people, as complex adaptive systems, are ambiguous, indeterminable, and inherently unpredictable. On the other, Machine Learning (ML) systems that claim to predict human behaviour are becoming ubiquitous in all spheres of social life. I contend that ubiquitous Artificial Intelligence (AI) and ML systems are close descendants of the Cartesian and Newtonian worldview in so far as they are tools that fundamentally sort, categorize, and classify the world, and forecast the future. Through the practice of clustering, sorting, and predicting human behaviour and action, these systems impose order, equilibrium, and stability to the active, fluid, messy, and unpredictable nature of human behaviour and the social world at large. Grounded in complexity science and enactive and embodied cognitive science approaches, this article emphasizes why people, embedded in social systems, are indeterminable and unpredictable. When ML systems "pick up" patterns and clusters, this often amounts to identifying historically and socially held norms, conventions, and stereotypes. Machine prediction of social behaviour, I argue, is not only erroneous but also presents real harm to those at the margins of society.
Identifiants
pubmed: 34529757
pii: 101872
doi: 10.1162/artl_a_00336
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
44-61Informations de copyright
© 2021 Massachusetts Institute of Technology.