Reproducibility in AI refers to the ability to consistently recreate a system’s behaviour or outputs under the same conditions—using the same code, data, configurations, and parameters. It is a scientific and operational cornerstone of AI assurance, supporting transparency, auditability, and trust.

A reproducible AI system allows:

• Internal teams to verify results across environments or teams

• External auditors to confirm system claims during evaluations

• Regulators to validate compliance and investigate failures

• Researchers and partners to replicate experiments or deployments

Reproducibility challenges often stem from:

• Lack of documentation or version control

• Randomness in model initialisation or data sampling

• Environmental dependencies (e.g., hardware, libraries, or APIs)

• Changes to data pipelines, preprocessing, or model updates

Assurance practices promote reproducibility by enforcing:

• Code and data versioning with tools like Git or DVC

• Use of containers or environment specifications (e.g., Docker, Conda)

• Logging of hyperparameters, seeds, and training configurations

• Standardised workflows and reproducible evaluation metrics

In mission-critical systems, reproducibility ensures that performance claims can be backed up by evidence and revalidated if the system fails or is challenged. It also supports post-incident analysis, regulatory reporting, and continuous improvement.

Regulatory frameworks and technical standards (e.g., ISO/IEC, NIST) increasingly recognise reproducibility as a criterion for trustworthy AI. Public sector and defence applications often require that deployed systems can be independently validated before use.