Advances in Data Analysis and Classification (2022) 16:823–846
https://doi.org/10.1007/s11634-021-00455-6


"This paper aims to contribute to the current discussion about AI by highlighting the relevance of statistical methodology in the context of AI development and application. Statistics can make important contributions to a more successful and secure use of AI systems, for example with regard to

1. 1. Design (Sect.  3 ): bias reduction; validation; representativity; selection of variables
2. 2. Assessment of data quality (Sect.  4 ): standards for the quality of diagnostic tests and audits; dealing with missing values
3. 3. Differentiation between causality and associations (Sect.  5 ): consideration of covariate effects; answering causal questions; simulation of interventions
4. 4. Assessment of certainty or uncertainty in results (Sect.  6 ): Increasing interpretability; mathematical validity proofs or theoretical properties in certain AI contexts; providing stochastic simulation designs; accurate analysis of the quality criteria of algorithms in the AI context"