Responsible AI in Real-Money Gaming: Embedding Regulatory Constraints in Personalization Algorithm - Vatsal Modi; Abhijit Chanda (Utilitas Mathematica, 2025)
The use of AI-powered personalization in real-money gaming has raised significant ethical and regulatory concerns in recent years. These systems are usually designed to maximize user en- gagement with the platform, which leads to higher monetization, but this sometimes comes at the cost of opaque and potentially discriminatory targeting systems, which risk exploiting user behavior, particularly among the vulnerable population. This study aims to address these issues by developing a regulatory-aligned framework that integrates ethical constraints such as fairness, transparency, and harm mitigation directly into the design of personalization algorithms. Draw- ing on secondary qualitative sources such as regulatory frameworks, peer-reviewed literature, and documented platform practices, this study analyzes current standards and gaps in industry practice. These insights inform the creation of the Responsible Personalization Framework (RPF), which operationalizes regulatory principles through concrete design patterns. The findings re- veal that widely adopted personalization strategies often contradict responsible AI principles. However, the proposed framework, featuring mechanisms like Harm-Aware Personalization and the Offer Parity Rule, offers actionable, regulation-compliant solutions for ethical personaliza- tion. Overall, the study demonstrates that embedding ethical and regulatory safeguards at the design stage, instead of post hoc reactive enforcement, enables the development of safer, fairer, and more responsible AI systems in the context of real-money gaming.