This Special Section aims to bring together contributions that leverage the latest advancement in AI to solve substance use and addiction-related problems (including gambling and other behavioural addictions). We prefer studies that build upon deep neural networks, the algorithms that underly all the recent breakthroughs in AI (such as most modern generative AI models including ChatGPT and DALL-E), but we would also consider studies that are based on traditional machine learning methods such as random forest.

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This Special Section aims to bring together key emerging evidence on NoLos within and across research groups globally. Specifically, we welcome papers with empirical data from any country/region on:

• Data that characterises the emergence of NoLo products, product availability and accessibility, and market characteristics;
• Data reporting/analysing the marketing and advertising of NoLos [e.g., analysing strategies used by industries (alcohol and others) to promote the consumption of NoLos, studies on how NoLo marketing impacts use outcomes];
• Attitudes and action of teenagers below the legal drinking age and of teenagers’ parents concerning underage teenagers’ consumption of NoLo beverages with alcohol brands and packaging,
• Data reporting and exploring patterns of purchase and consumption and links with social, commercial and political determinants of health (tactics used by for-profit industries), and the drivers of purchase and consumption among particular population groups (young people, people who are pregnant; abstainers/people seeking to reduce their ethanol intake) with implications for harm/benefit and policy.