This scenario is a direct application of the Reliability and Safety principle. This principle mandates that AI systems must be robust, resilient, and operate dependably, especially when faced with unexpected conditions or "edge cases." Data fields with unusual or missing values represent such unexpected conditions. By being designed to not provide a prediction in this situation, the system avoids generating a potentially inaccurate, nonsensical, or harmful output. This "fail-safe" behavior is a core component of ensuring the system is reliable and safe for its users.

Microsoft. (n.d.). The six principles of responsible AI. Microsoft Learn. Retrieved October 24, 2025, from https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai

Reference: In the "Reliability and safety" section, it is stated: "AI systems need to be reliable and safe... To be reliable, a system should operate as it was originally designed... This requires rigorous testing and validation to ensure that the system responds safely to edge cases..." Missing or unusual values are a classic "edge case."

Microsoft. (2024, September 12). Responsible AI in practice. Microsoft Learn. Retrieved October 24, 2025, from https://learn.microsoft.com/en-us/training/modules/responsible-ai-practice/2-understand-principles

Reference: In the "Reliability and safety" section, the text notes that systems must be "resilient to failure or unexpected conditions." The module emphasizes that a reliable system must "work as expected," and providing a flawed prediction on missing data would be a failure to work as expected.

Zhang, J., Liu, M., & Liu, X. (2023). Reliability of Machine Learning: A Survey. ACM Computing Surveys, 55(9), Article 189. https://doi.org/10.1145/3555806

Reference: Section 3.1, "Data Quality Issues," explicitly identifies "missing values" and "anomalies" as key challenges to machine learning reliability. The paper discusses (Section 4.1) "failure detection" mechanisms, where a system identifies inputs it cannot process reliably, as a strategy to ensure overall system reliability. Refusing to predict is such a mechanism.