2022/08/22 by Cyrille Imbert, Vincent Ardourel
Decision Sciences · Computer Science · #Scientific Computing and Data Management #Computability, Logic, AI Algorithms #Explainable Artificial Intelligence (XAI)
paper · pdf · doi:10.1017/psa.2022.78
Abstract Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity of computational science: Profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code.