Machine learning in tutorials. Universal applicability, underinformed application, and other misconceptions.

Autor/inn/en	Heuer, Hendrik; Jarke, Juliane; Breiter, Andreas
Titel	Machine learning in tutorials. Universal applicability, underinformed application, and other misconceptions.
Quelle	In: Big data & society, 8 (2021) 1, S. 1-13 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben; Abbildungen
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	2053-9517
DOI	10.1177/20539517211017593
Schlagwörter	Selbstbildung; Datenerhebung; Soziale Ungleichheit; Soziales System; Digitale Medien; Künstliche Intelligenz; Algorithmus; Künstliche Intelligenz; Soziale Ungleichheit; Soziales System; Informationssystem; Datenanalyse; Digitalisierung; Datenauswertung; Datenaufbereitung; Datenerhebung; Algorithmus; Datenanalyse; Datenauswertung; Digitalisierung; Selbstlernen; Datenaufbereitung; Informationssystem
Abstract	Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of (new) social inequalities through machine learning-based systems. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1