Is there a role for statistics in artificial intelligence?

TL;DR

This paper argues that statistics is fundamentally important to AI, contributing to its theoretical foundation, practical development, and educational curricula, emphasizing its interdisciplinary role.

Contribution

It highlights the essential role of statistical methodology in AI, covering methodological development, study design, data quality, causality, and uncertainty assessment.

Findings

Statistics enhances AI's theoretical understanding.

Statistical methods improve AI research design and data analysis.

Integration of statistics into AI education is crucial.

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 contributing to the current discussion by 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 deals with the equally necessary and meaningful extension of curricula in schools and universities.