Evaluations in fields like AI benchmarking and academic grading are often manipulated to produce favorable results without genuine improvement—a practice known as "gaming." This undermines trust in assessments, distorts incentives, and can lead to poor decisions, such as deploying flawed AI models. While evaluators and those gaming the system engage in an arms race, existing solutions tend to focus on narrow technical fixes rather than a holistic approach.

A Multi-Layered Defense Against Gaming

One way to address this could involve combining several strategies to make evaluations more resistant to manipulation:

• Technical safeguards: Metrics could be designed to detect adversarial inputs or overfitting, using tools like adversarial robustness testing.
• Procedural adjustments: Randomizing test conditions, blinding evaluators to submission sources, and using multiple evaluation methods could reduce predictability.
• Transparency and oversight: Open evaluation frameworks with audit logs and third-party verification might discourage manipulation.
• Continuous updates: A feedback loop where new gaming tactics are identified and countered, possibly through community contributions, could keep defenses adaptive.

These components could be integrated into a software platform, allowing users to configure evaluations with built-in anti-gaming measures.

Potential Applications and Stakeholders

This approach could benefit:

• AI researchers and companies by ensuring fair model comparisons.
• Benchmark organizers (e.g., MLPerf) by maintaining credibility.
• Regulators who rely on trustworthy evaluations for policy decisions.
• End users who interact with rigorously tested systems.

Stakeholder incentives vary—evaluators want reliable results, while model developers may resist added complexity unless they see value in fair competition. Third-party auditors could be motivated by demand for independent verification.

Execution and Adaptation

An initial version might focus on adversarial robustness testing for AI benchmarks, integrating existing libraries like CleverHans with new detection methods. Partnering with a benchmark organization for a pilot could validate the approach. Over time, features like randomization tools and audit logs could be added, alongside a community forum for reporting new gaming tactics.

Existing solutions like Dynabench or MLPerf address parts of the problem (e.g., dynamic datasets or strict submission rules), but a more comprehensive system could combine technical, procedural, and social defenses while remaining adaptable to new gaming strategies.