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DSM systems like IRIS should meet very high safety thresholds, typically >95% overall accuracy, extremely low false-negative rates, and tightly controlled false positives. Compliance with ISO 26262 (functional safety) and ISO 21448 (SOTIF) should be mandatory, along with proven reliability across diverse demographics and real-world conditions.

For commercial deployment in safety-critical contexts, fatigue detection systems should meet ≥95% sensitivity and specificity, ≤5% false-negative and false-positive rates, validated across diverse real-world conditions, with continuous post-market performance monitoring.

High level of performance with accuracy and reliability as high as the defense line of my football club

Because IRIS is a safety system used while driving, it must achieve very high accuracy and reliability before commercial release. It should detect drowsiness correctly in most situations while avoiding unnecessary false alerts. Just as important, it must work consistently for different drivers and environments. Strong real-world testing is essential because even small errors could affect safety.

Commercial fatigue-detection systems should meet safety-critical standards with ~95%+ accuracy, false negatives below 1–2%, controlled false positives, and compliance with ISO functional safety and real-world validation requirements.

Better system mobility on all races and skin complexions

The Bottom Line: Success in any project relies on clear communication and consistent action rather than waiting for the "perfect" moment. By breaking down complex goals into manageable pillars—such as prioritizing high-impact tasks, maintaining a feedback loop with your team, and focusing on incremental progress—you create a sustainable workflow that prevents burnout. Ultimately, the goal is to balance efficiency with quality; as long as the core objective remains the "North Star," small daily adjustments will naturally lead to the desired outcome.

Good resulting tests on sample groups of people from all backgrounds, that have a negligible rate of false positives/negatives.

Given the safety-critical nature of Driver State Monitoring (DSM) systems like IRIS, regulatory and industry expectations should be set significantly higher than typical consumer AI applications. Both false positives and false negatives carry risks: false positives may lead to unnecessary driver distraction or system disengagement, while false negatives may directly contribute to road accidents. Although the EU AI Act does not prescribe explicit numerical thresholds for accuracy, it requires high-risk AI systems to achieve a level of performance that is appropriate to their intended purpose and foreseeable risks. For IRIS, this implies: High sensitivity (recall) for detecting genuine drowsiness, to minimise false negatives. Acceptable specificity, to avoid excessive false alerts that may cause alert fatigue. Robust performance across environments, including low light, occlusions (glasses, hats), and varied camera angles. Consistent performance across demographic groups, with minimal disparity between protected characteristics. In practice, commercial deployment should align with automotive safety standards (e.g. ISO 26262, ISO 21448 – Safety of the Intended Functionality) and internal thresholds defined through rigorous validation testing. Regulators are likely to expect documented trade-offs between false positives and false negatives, rather than perfect accuracy.

False negative

False negative

Only systems that demonstrate high accuracy, minimal missed detections, controlled false alarms, and compliance with safety standards should be approved for commercial use.

For a system like IRIS, “high accuracy” is not sufficient. Commercial deployment should only be permitted

More data needs to be collected to correct the issues of false positives. An excellent level of regulation should he deployed into these areas where Artificial intelligence could make or mar people's lives.

Quality of materials/programs used, Quality Control

For commercial deployment, the system should be required to be as accurate as possible. Deploying a software that has a high probability of making errors would be catastrophic to road safety and thr protection of customers/users of the software.