In the realm of road vehicles, the safety of AI-based systems is inextricably linked to the data used to develop them. ISO/PAS 8800 (Road Vehicles — Safety and Artificial Intelligence) provides a framework for ensuring that data integrity and quality are maintained throughout the AI lifecycle. Unlike traditional software where logic is explicitly coded, AI systems 'learn' from data, making the quality of that data a primary safety concern.

2. Key Definitions

• Data Integrity: The assurance that data remains accurate, complete, and consistent throughout its entire lifecycle, from collection to decommissioning.
• Data Quality: The fitness of data for its intended purpose in training, validating, and testing AI models within a specific Operational Design Domain (ODD).

3. Data Quality Dimensions under ISO/PAS 8800

To comply with safety standards, data must be evaluated against several dimensions:

• Representativeness: The data must cover the full range of scenarios the vehicle will encounter in its ODD, including rare 'corner cases'.
• Accuracy and Precision: The sensors used for collection must be calibrated, and the ground-truth labels must be verified for correctness.
• Completeness: There should be no missing values or gaps in the datasets that could lead to biased or unpredictable AI behavior.
• Timeliness: For dynamic environments, data must reflect current road conditions, signage standards, and traffic laws.

4. The Data Lifecycle and Safety

ISO/PAS 8800 emphasizes a rigorous data pipeline:

1. Acquisition: Capturing raw data using high-fidelity sensors.
2. Preparation & Cleaning: Removing noise and handling outliers without stripping away critical safety information.
3. Labeling/Annotation: Ensuring that human or automated labelers follow strict guidelines to avoid 'label noise'.
4. Augmentation: Using synthetic data to fill gaps in real-world data, provided the synthetic data is physically realistic.
5. Mitigating Bias

Bias in data is a significant safety risk. If a training set lacks diversity (e.g., only contains daytime driving), the AI may fail in low-light conditions. ISO/PAS 8800 requires documented processes to identify and mitigate technical and cognitive biases to ensure the Intended Functionality (SOTIF) is safe.

6. Summary

Data integrity is not just a technical requirement; it is a safety-critical pillar. High-quality data ensures that the resulting AI model is robust, reliable, and capable of operating safely in complex automotive environments.