Generative AI and Behavioural Data Science

doi:10.1017/9781108939010.016

11 - Generative AI and Behavioural Data Science

from Part III - Algorithmic Behaviour

Published online by Cambridge University Press: 29 May 2026

Ganna Pogrebna

Edited by

Ganna Pogrebna and

Thomas T. Hills

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Ganna Pogrebna: Affiliation:
The Alan Turing Institute
Thomas T. Hills: Affiliation:
University of Warwick

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Summary

The recent surge in conversational AI has opened up new avenues for the application of Behavioural Data Science, with Generative AI, particularly LLM models such as ChatGPT, representing a promising platform for analysing human behaviour. This chapter provides an overview of the role of Generative AI models in Behavioural Data Science, highlighting their strengths and limitations. The chapter begins by introducing the principles of Generative AI models and their potential applications in Behavioural Data Science. It discusses the advantages of using Generative AI models to study human behaviour, such as their ability to analyse large volumes of unstructured data and their capacity to learn from interactions with users. The chapter then describes the different ways in which Generative AI models can be used in Behavioural Data Science, such as analysing sentiment, predicting behaviour and generating insights from user interactions. It discusses the challenges associated with using Generative AI models, such as the need for accurate training data and the potential for bias in the model. The chapter also addresses the ethical concerns associated with the use of Generative AI models, such as privacy violations and the potential for unintended consequences. It discusses ways to address these concerns, such as implementing transparency and explainability in Generative AI models. The chapter concludes by discussing the future of Generative AI models in Behavioural Data Science, highlighting the potential for interdisciplinary collaboration with other fields such as psychology and sociology. It emphasises the need for continued development and refinement of Generative AI models and their associated methods for studying human behaviour. This chapter provides an overview of the role of Generative AI models in Behavioural Data Science, highlighting their strengths and limitations. It serves as a valuable resource for researchers and practitioners in the field who are interested in utilising Generative AI models to analyse and understand human behaviour.

Keywords

generative AI LLM ChatGPT conversational AI behavioural data science sentiment analysis predictive modelling

Information

Type: Chapter
Information: The Cambridge Handbook of Behavioural Data Science , pp. 185 - 203
DOI: https://doi.org/10.1017/9781108939010.016 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2026

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