Gaming commission hears UNLV study on AI in gambling, urges more oversight and transparency

The Massachusetts Gaming Commission on Nov. 6 heard a yearlong UNLV International Gaming Institute study that mapped how artificial intelligence is already used across the gambling industry and where risks to consumers may appear.

The study, presented by Dr. Kazra Gaharian and introduced by the commission’s research director, found that AI use in the industry clusters around four themes — operational efficiency and workforce augmentation, customer‑relationship management, player experience and engagement, and compliance and risk. It recommended targeted information‑gathering by regulators, industry training, and the appointment of an internal AI lead to track rapidly evolving use cases.

Why it matters: As operators scale generative AI and machine‑learning tools, regulators face new questions about transparency, efficacy and whether tools used for marketing or customer retention could cross lines into manipulative behavior. The UNLV team recommended standardized reporting on indicators of gambling harm, more open data and sector‑specific regulatory guidance.

Among the study’s findings, the UNLV team reported that financial‑transaction indicators (for example, deposits and withdrawals) tended to have stronger evidence supporting their use in risk models than some commonly‑cited play metrics. The team also found that usage of responsible‑gaming (RG) tools is under‑studied as an indicator, creating a knowledge gap regulators should address.

Dr. Gaharian emphasized the pace of change. "The pressures to get new content out at speed is huge. AI is perfect," he said, illustrating how generative models can scale functions that once required large teams. He also raised the EU AI Act as an example of a use‑case regulatory approach that may have extra‑territorial effects on the industry.

Commissioners pressed the researchers on concrete next steps for Massachusetts. Commissioner O’Brien asked what the state could do to increase evidence around responsible‑gaming AI. Commissioner Skinner pressed for transparency around commercial reg‑tech models and for the commission to develop a benchmarking approach that could assess vendor efficacy without exposing proprietary trade secrets.

The commission’s staff said it already has an internal AI workgroup and will continue to partner with UNLV on the systematic review (the “bridge” database) to keep pace with new research and commercial developments. The UNLV team recommended pilot programs for financial‑risk assessments and cautioned that cross‑operator data sharing — often necessary for robust financial screening — raises privacy and practical obstacles in the U.S. market.

Next steps: Commissioners urged more detailed briefings and a possible standalone meeting to dig into policy choices. The commission will continue tracking UNLV’s open research outputs and pursue dialogue with industry and other regulators about standardized indicators and transparency requirements.