Algorithm
This is the risk of the ML algorithm, model architecture, optimization technique, or other aspects of the training process being unsuitable for the intended application.Since these are key decisions that influence the final ML system, we capture their associated risks separately from design risks, even though they are part of the design process
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
1 - Pre-deployment
Risk ID
mit190
Domain lineage
7. AI System Safety, Failures, & Limitations
7.3 > Lack of capability or robustness
Mitigation strategy
1. Conduct a rigorous, pre-deployment Model Selection Governance review, including comparative performance analysis across multiple suitable model architectures and optimization techniques, to formally validate the appropriateness of the chosen ML system for the specific application and operating constraints. 2. Implement comprehensive Adversarial Robustness Testing and Stress-Testing against diverse and non-standard input scenarios to ensure the selected algorithm and architecture maintain performance integrity and stability under real-world pressures and edge cases. 3. Integrate Explainable AI (XAI) mechanisms and mandate independent Algorithmic Audits to ensure that the fundamental design rationale and internal operation of the ML system are transparent and auditable, verifying its alignment with ethical standards and intended functional requirements.