In AI development, a "seed" ensures that random processes (like weight initialization) are reproducible. If an A/B test compares two models using different seeds, the auditor cannot tell if the performance difference is due to the model changes or simply due to "random luck" in how the weights were initialized. This invalidates the test results. For a fair "apple-to-apples" comparison, the seed should remain consistent. Tuning on a training set (Option B) is standard, though it risks overfitting; however, the lack of scientific control in testing (Option C) is a more immediate risk to the integrity of the change management process.