Multimodal Generative AI and Foundation Models for Behavioural Health in Online Gambling
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917946607009792 |
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| author | Samsel, Konrad Noaeen, Mohammad Seeman, Neil Keshavjee, Karim Li, Li-Jia Shakeri, Zahra |
| author_facet | Samsel, Konrad Noaeen, Mohammad Seeman, Neil Keshavjee, Karim Li, Li-Jia Shakeri, Zahra |
| contents | Online gambling platforms have transformed the gambling landscape, offering unprecedented accessibility and personalized experiences. However, these same characteristics have increased the risk of gambling-related harm, affecting individuals, families, and communities. Structural factors, including targeted marketing, shifting social norms, and gaps in regulation, further complicate the challenge. This narrative review examines how artificial intelligence, particularly multimodal generative models and foundation technologies, can address these issues by supporting prevention, early identification, and harm-reduction efforts. We detail applications such as synthetic data generation to overcome research barriers, customized interventions to guide safer behaviors, gamified tools to support recovery, and scenario modeling to inform effective policies. Throughout, we emphasize the importance of safeguarding privacy and ensuring that technological advances are responsibly aligned with public health objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Generative AI and Foundation Models for Behavioural Health in Online Gambling Samsel, Konrad Noaeen, Mohammad Seeman, Neil Keshavjee, Karim Li, Li-Jia Shakeri, Zahra Computers and Society Online gambling platforms have transformed the gambling landscape, offering unprecedented accessibility and personalized experiences. However, these same characteristics have increased the risk of gambling-related harm, affecting individuals, families, and communities. Structural factors, including targeted marketing, shifting social norms, and gaps in regulation, further complicate the challenge. This narrative review examines how artificial intelligence, particularly multimodal generative models and foundation technologies, can address these issues by supporting prevention, early identification, and harm-reduction efforts. We detail applications such as synthetic data generation to overcome research barriers, customized interventions to guide safer behaviors, gamified tools to support recovery, and scenario modeling to inform effective policies. Throughout, we emphasize the importance of safeguarding privacy and ensuring that technological advances are responsibly aligned with public health objectives. |
| title | Multimodal Generative AI and Foundation Models for Behavioural Health in Online Gambling |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2503.03752 |