To what extent can we trust a machine? Establishing trustworthiness guidelines to evaluate machine learning models across chemistry contexts - Forschungsportal der Justus-Liebig-Universität Gießen:

Journalartikel

To what extent can we trust a machine? Establishing trustworthiness guidelines to evaluate machine learning models across chemistry contexts

Autorenliste: Martin, PP; Dood, A; Watts, F; Shultz, G; Graulich, N

Jahr der Veröffentlichung: 2026

Zeitschrift: Journal of Research in Science Teaching

Verlag: Wiley

Autoren/Herausgeber

- Uni.-Prof. Dr. Nicole Graulich

Zitierstile

Harvard-Zitierstil: Martin, P., Dood, A., Watts, F., Shultz, G. and Graulich, N. (2026) To what extent can we trust a machine? Establishing trustworthiness guidelines to evaluate machine learning models across chemistry contexts, Journal of Research in Science Teaching

APA-Zitierstil: Martin, P., Dood, A., Watts, F., Shultz, G., & Graulich, N. (2026). To what extent can we trust a machine? Establishing trustworthiness guidelines to evaluate machine learning models across chemistry contexts. Journal of Research in Science Teaching.