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The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling

Kasra Ghaharian, Simo Dragicevic, Chris Percy, Sarah E. Nelson, W. Spencer Murch, Robert M. Heirene, Kahlil Simeon-Rose, Tracy Schrans

TL;DR

The paper addresses the lack of standardized benchmarks for evaluating AI-enabled player risk detection in gambling. It advocates a conceptual benchmarking framework built on three core dimensions—Time, Engagement Level, and Gambling Vertical—and outlines a suite of benchmark datasets, data-management requirements, and result-validation mechanisms. It discusses governance, dataset size and diversity, target-variable definitions, and the need for dynamic benchmarks to handle a evolving gambling landscape. The authors argue that such benchmarking is crucial for transparency, regulatory trust, and fostering responsible AI adoption in gambling harm prevention, with potential extensions to related domains like RG interventions, AML, and integrity monitoring.

Abstract

Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing concerns around transparency and effectiveness have highlighted the absence of standardized methods for evaluating the quality and impact of these tools. This makes it impossible to gauge true progress; even as new systems are developed, their comparative effectiveness remains unknown. We argue the critical next innovation is developing a framework to measure these systems. This paper proposes a conceptual benchmarking framework to support the systematic evaluation of player risk detection systems. Benchmarking, in this context, refers to the structured and repeatable assessment of artificial intelligence models using standardized datasets, clearly defined tasks, and agreed-upon performance metrics. The goal is to enable objective, comparable, and longitudinal evaluation of player risk detection systems. We present a domain-specific framework for benchmarking that addresses the unique challenges of player risk detection in gambling and supports key stakeholders, including researchers, operators, vendors, and regulators. By enhancing transparency and improving system effectiveness, this framework aims to advance innovation and promote responsible artificial intelligence adoption in gambling harm prevention.

The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling

TL;DR

The paper addresses the lack of standardized benchmarks for evaluating AI-enabled player risk detection in gambling. It advocates a conceptual benchmarking framework built on three core dimensions—Time, Engagement Level, and Gambling Vertical—and outlines a suite of benchmark datasets, data-management requirements, and result-validation mechanisms. It discusses governance, dataset size and diversity, target-variable definitions, and the need for dynamic benchmarks to handle a evolving gambling landscape. The authors argue that such benchmarking is crucial for transparency, regulatory trust, and fostering responsible AI adoption in gambling harm prevention, with potential extensions to related domains like RG interventions, AML, and integrity monitoring.

Abstract

Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing concerns around transparency and effectiveness have highlighted the absence of standardized methods for evaluating the quality and impact of these tools. This makes it impossible to gauge true progress; even as new systems are developed, their comparative effectiveness remains unknown. We argue the critical next innovation is developing a framework to measure these systems. This paper proposes a conceptual benchmarking framework to support the systematic evaluation of player risk detection systems. Benchmarking, in this context, refers to the structured and repeatable assessment of artificial intelligence models using standardized datasets, clearly defined tasks, and agreed-upon performance metrics. The goal is to enable objective, comparable, and longitudinal evaluation of player risk detection systems. We present a domain-specific framework for benchmarking that addresses the unique challenges of player risk detection in gambling and supports key stakeholders, including researchers, operators, vendors, and regulators. By enhancing transparency and improving system effectiveness, this framework aims to advance innovation and promote responsible artificial intelligence adoption in gambling harm prevention.

Paper Structure

This paper contains 26 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Machine Learning for Player Risk Detection
  3. Current Problems
  4. Broad Challenges for Stakeholders
  5. Specific Challenges for Procuring or Commissioning Models
  6. System Consequences
  7. Benchmark Datasets and Performance Benchmarking
  8. Background
  9. Types of Benchmarking
  10. The State of Benchmarking in Player Risk Detection
  11. A Proposed Benchmarking Framework for Player Risk Detection Systems
  12. Core Dimensions
  13. Example Benchmark Datasets
  14. Requirements
  15. Data collection
  16. ...and 11 more sections

Figures (2)

  • Figure 1: Core Dimensions of the Benchmarking Suite.
  • Figure 2: Examples of Benchmark Datasets.