From rules to examples

Machine learning's type of authority

Dados Bibliográficos

ID5260375
AutoresAlexander Campolo (0000-0003-3159-4131, Durham University, autor correspondente), Katia Schwerzmann (0000-0002-7938-2608, Ruhr University Bochum)
Ano2023
Volume10
Fascículo2
Data de publicação2023-07-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoBig Data & Society (JOURNAL)
Identificadores do periódicoISSN: 2053-9517 • E-ISSN: 2053-9517
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1177/20539517231188725
OpenAlexW4386716152
IdiomaEN
Citações recebidas17
Referências citadas29

This paper analyzes the effects of a perceived transition from a rule-based computer programming paradigm to an example-based paradigm associated with machine learning. While both paradigms coexist in practice, we critically discuss the distinctive epistemological and ethical implications of machine learning's "exemplary" type of authority. To capture its logic, we compare it to computer programming rules that date to the middle of the 20th century, showing how rules and examples have regulated human conduct in significantly different ways. In contrast to the highly constructed, explicit, and prescriptive form of authority imposed by programming rules, machine learning models are trained using data that has been made into examples. These examples elicit norms in an implicit, emergent manner to make prediction and classification possible. We analyze three ways that examples are produced in machine learning: labeling, feature engineering, and scaling. We use the phrase "artificial naturalism" to characterize the tensions of this type of authority, in which examples sit ambiguously between data and norm

Deep learning · Epistemology · Feature engineering · Machine learning · Naturalism · Phrase · Adversarial Robustness in Machine Learning · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Artificial Intelligence