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The rapid growth in real-world applications of machine learning (ML) and artificial intelligence (AI) has increased the demand for integrating behaviour science into both the development and deployment of these tools, and there is a growing demand to directly use ML and AI in behaviour science applications. Applied behaviour science is expanding its analytical repertoire correspondingly. Instead of studying individual-level behaviour units, behaviours and their determinants are described as complex behaviours and holistic systems. Previously unavailable AI and ML tools have been adopted in a new class of digital behaviour change interventions (DBCI) and provide new functionality in data analytics, personalization and real time feedback. The emerging behaviour-change theories combined with the technology have a meaningful potential to improve our achieving objectives in changing critical behaviours and economic outcomes directly.
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