Statistical Methods in Generative Artificial Intelligence

Generative artificial intelligence (AI) is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sampling from probabilistic models, and, by default, they come with no guarantees about correctness, safety, fairness, or other properties. Statistical methods offer a promising approach to improve the reliability of generative AI techniques. In addition, statistical methods are also promising for improving the quality and efficiency of AI evaluation, as well as for designing interventions and experiments in AI. In this article, we review some of the existing work on these topics, explaining both the general statistical techniques used and their applications to generative AI. We also discuss limitations and potential future directions.

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Publication Details

Journal
Annual Review of Statistics and Its Application
Published
2026-08-14
DOI
https://doi.org/10.1146/annurev-statistics-042825-121454
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Statistical Methods in Generative Artificial Intelligence

Edgar Dobriban
Annual Review of Statistics and Its Application
Generative Adversarial Networks and Image Synthesis
article

Statistical Methods in Generative Artificial Intelligence

Edgar Dobriban
article en

Abstract

Generative artificial intelligence (AI) is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sampling from probabilistic models, and, by default, they come with no guarantees about correctness, safety, fairness, or other properties. Statistical methods offer a promising approach to improve the reliability of generative AI techniques. In addition, statistical methods are also promising for improving the quality and efficiency of AI evaluation, as well as for designing interventions and experiments in AI. In this article, we review some of the existing work on these topics, explaining both the general statistical techniques used and their applications to generative AI. We also discuss limitations and potential future directions.

Annual Review of Statistics and Its Application
Philadelphia University (US)
Openalex Percentile: Top 11%
Generative Adversarial Networks and Image Synthesis
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