Is there a role for statistics in artificial intelligence?

Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans A. Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland, Adalbert Wilhelm and Tim Friede ()
Additional contact information
Sarah Friedrich: University Medical Center Göttingen
Gerd Antes: University of Freiburg
Sigrid Behr: Novartis Pharma AG
Harald Binder: University of Freiburg
Werner Brannath: University Bremen
Florian Dumpert: Federal Statistical Office of Germany
Katja Ickstadt: TU Dortmund University
Hans A. Kestler: Ulm University
Johannes Lederer: Ruhr-Universität Bochum
Heinz Leitgöb: Department of Sociology, University of Eichstätt-Ingolstadt
Markus Pauly: TU Dortmund University
Ansgar Steland: RWTH Aachen University
Adalbert Wilhelm: Jacobs University Bremen
Tim Friede: University Medical Center Göttingen

Advances in Data Analysis and Classification, 2022, vol. 16, issue 4, No 2, 823-846

Abstract: Abstract The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.

Keywords: Statistics; Artificial intelligence; Machine learning; Data science; 68T01; 62-02 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11634-021-00455-6

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