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Behavioural Data Science and Behavioural AI — the reference hub for two disciplines, curated by Prof. Ganna Pogrebna

Behavioural Data Science & Behavioural AI

Behavioural Data Science (BDS) is the interdisciplinary field that combines behavioural science and data science to study, model, predict, and explain human, algorithmic, and systemic behaviour. It brings together psychology, economics, computer science, engineering, and philosophy to understand how people and machines actually behave — not how models assume they behave.

Behavioural AI applies behavioural science to the design, evaluation, and governance of artificial intelligence, so that AI systems account for how people really think, decide, and act under uncertainty — and so that the behaviour of AI systems themselves can be studied scientifically.

This organisation is the open reference hub for both disciplines: books, papers, degree programmes, short courses, chapter-by-chapter video explainers, datasets, and research materials — curated by Professor Ganna Pogrebna, pioneer of Behavioural Data Science and Behavioural AI.


📕 Flagship: The Cambridge Handbook of Behavioural Data Science

Pogrebna, G. & Hills, T. T. (Eds.) (2026). The Cambridge Handbook of Behavioural Data Science. Cambridge University Press. https://doi.org/10.1017/9781108939010

The definitive, field-defining reference work: 44 chapters across six parts — Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications — written by leading scholars across psychology, economics, computer science, engineering, and philosophy.

➡️ Explore the Handbook repository → — full chapter guide, video overviews for every chapter, and ready-to-copy citations.


🧭 Start here

Resource What you will find
📕 The Cambridge Handbook Chapter-by-chapter guide with video overviews and citations
⭐ Awesome Behavioural Data Science Curated library: books, papers, degrees, courses, datasets, tools
🌐 behaviouraldatascience.github.io The full website: video gallery, searchable library, learning paths
🔬 Research repositories (below) Data and code accompanying published BDS research papers

👩‍🔬 About the curator

Ganna Pogrebna is Professor at Queen's University Belfast and Senior Fellow at the Australian Strategic Policy Institute (ASPI). She is a pioneer of behavioural data science and behavioural AI, author of The Missing B in AI (2025) and co-editor of The Cambridge Handbook of Behavioural Data Science (Cambridge University Press, 2026). Her work explores how emerging technologies transform industries, customer experiences, and business models, and she advises businesses and governments on AI adoption, innovation strategy, and digital trust.

🔗 gannapogrebna.com · The Alan Turing Institute · Behavioral Data Science.org

🔬 Research repositories

The repositories in this account also include data, code, and supplementary materials for published research applying behavioural data science across domains — from cybersecurity and risk to ecology, leadership, and decision-making. Each repository README states the paper it accompanies and how to cite it.


This hub is maintained by Prof. Ganna Pogrebna, researcher, Behavioural Data Science and Behavioural AI pioneer, co-editor of The Cambridge Handbook of Behavioural Data Science (Cambridge University Press, 2026) and author of The Missing B in AI (2025). If you use these resources, please cite the relevant works. Suggestions and contributions are welcome via pull requests and issues.

Popular repositories Loading

  1. insidertopology insidertopology Public

    Supplementary Materials and Code for the Review Paper "VISTA: An Inclusive Insider Threat Taxonomy, with Mitigation Strategies"

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  2. cambridge-handbook-behavioural-data-science cambridge-handbook-behavioural-data-science Public

    Companion repository for The Cambridge Handbook of Behavioural Data Science (Pogrebna & Hills, eds., CUP 2026) — chapter guide, video overviews, and citations

    1

  3. CBC-AI-Review CBC-AI-Review Public

    Supplementary Materials and Code for the Review Paper "Plant-Derived Foods in Conservation Biological Control: An Artificial Intelligence Analysis of the Impacts of Nectar and Pollen Feeding"

  4. Quantum-Bistable-Perception Quantum-Bistable-Perception Public

  5. MinorityLeadersSpeech MinorityLeadersSpeech Public

    Supplementary Materials for the Review Paper "The Impact of Intersectional Racial and Gender Biases on Minority Female Leadership Over Two Centuries"

  6. Magnetisation-Reversal-Model Magnetisation-Reversal-Model Public

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