Accuracy

Mitigation strategy

1. Establish a Continuous and Rigorous Technical Validation Program Implement a program of continuous and rigorous validation, including stress testing and adversarial testing (e.g., AI red teaming), to systematically identify and rectify vulnerabilities, data drift, and edge cases that could compromise the system's predictive accuracy and robustness across diverse operating environments. 2. Mandate Ongoing Data Integrity and Quality Assurance Institute proactive and continuous data auditing and validation pipelines to ensure the integrity, quality, and representativeness of all training and retraining datasets. This is essential to prevent the introduction of corrupted, skewed, or low-quality data, which is a primary factor in the degradation of model accuracy and the manifestation of a lack of robustness. 3. Integrate Robustness-Enhancing Techniques in Model Design Incorporate intrinsic robustness mechanisms, such as adversarial training and the use of robustness-aware loss functions, directly into the model development lifecycle. These techniques are designed to enhance the model's resilience to input perturbations and prevent the model from over-relying on brittle statistical dependencies, thereby ensuring more stable and correct predictions.