![]() For computation-based science, this includes open and impartial scrutiny of the computational model by peers in the field, and the creation of unbiased and reproducible tests to justify a belief by others that the model (and its instantiation in both software and hardware) actually corresponds to some truth or physical reality. An essential part of the scientific method, it seeks to evaluate the truth and justification of a scientific belief. While not sexy, the validation process ensures that responsible research is being performed and that legitimate results are being produced, published and promoted. Validation is a critical part of the scientific process and of scientific computation. In my Scientific Computing article, “ Validation: Assessing the Legitimacy of Computational Results” I noted the extreme importance of open and impartial scrutiny by peers in the field: For example drones and self-driving cars operate in true life-and-death situations where biases in a validation test can result in collisions with people, property, and other vehicles. With the advent of deep-learning, open and impartial validation of complex learning and adaptive systems is becoming ever more important.
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