An adoptable AI use case registry for the gambling industry

At its core, the registry is designed to support a structured record of the AI use cases across an organization's gambling operations — capturing what each use case is and how it is governed, so that it can be reviewed and overseen.

The registry is intended to support responsible innovation by creating a "living" structured mechanism for various stakeholders and organizations to log and describe how AI systems are being used (i.e., "use cases"), what potential impacts they may have, and what governance measures are in place to support oversight, accountability, and player protection.

In practice, organizations can adopt this template as their own and host it on their own infrastructure, maintaining an internal inventory of the AI use cases across their gambling operations.

Existing transparency and reporting frameworks offer foundational principles, but they are not tailored to the nuances of the gambling industry. This registry was designed by drawing inspiration from those broader tools, but refined through direct engagement with gambling sector stakeholders to ensure it is fully contextualized for this unique landscape.

For Industry (Operators, Suppliers, etc.): It provides a structured internal framework to map, monitor, and manage AI deployments, ensuring alignment with corporate governance, risk management protocols, and evolving compliance standards.

For Regulators: It acts as a standardized tool that could be recommended or mandated, offering the visibility needed to supervise industry practices, uphold legislation, and protect consumers.

• Adopt it at any scale: roll it out across an entire organization, run it within a single department or team, or use it individually to document your own AI use.
• Document each AI use case in one place — what it is, where it operates (department), who owns it (contact), and its deployment status — so your inventory stays auditable over time.
• Maintain it as a living record — revisit a use case and its screening whenever the underlying system is materially changed or repurposed.
• Give internal stakeholders visibility into your AI use — including who owns each system and who to contact about it — and share redacted records externally as a transparency mechanism for regulators, auditors, or the public.

Because the fields provided may not perfectly accommodate or be relevant to every single AI use case, we recommend making a best effort to complete all sections. If you are uncertain whether a specific field applies, we advise erring on the side of caution and conservative disclosure by filling it out.

The registry is not currently intended to function as a mandatory regulatory requirement, certification system, or approval process. Instead, it is intended to support information-sharing, governance readiness, and cross-stakeholder understanding.

AI System

An AI system is a computational or machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments and players. This may include machine learning systems, generative AI, predictive analytics, behavioral models, recommendation systems, or advanced automated decision-support systems.

This definition is a recommended baseline, adapted from the OECD Recommendation on AI and the NIST AI Risk Management Framework. Adopting organizations may expand or adapt it to reflect their own context and risk appetite, but should avoid narrowing it below this scope.

Use Case

A use case refers to a specific operational application of an AI system. A single AI system may support multiple use cases that fall into different impact tiers based on how the system is being used.

Player Interaction & Impact

The extent to which an AI system's outputs affect individual players — whether applied automatically or acted on by a person. This ranges from interaction (targeting or communicating with players, or analyzing their behavior or data) to material impact (consequentially affecting their welfare, financial outcomes, access, behavior, fairness, autonomy, or regulatory treatment). Where a use case falls on this spectrum is what the screening assesses to assign its documentation tier.

Documentation Tier

The documentation level assigned to a use case based on its potential impact on players. The tiers are additive — higher tiers require additional documentation on top of the tiers below.