Machine learning—essentially a computer that recognizes patterns without having to be explicitly programmed—is revolutionizing many industries. Machine learning enables us to find answers and unexpected relationships in data that were impossible to find with the “cookbook recipe” style of programming that currently powers our software.
However, there is a downfall to the use of machine learning: the “black box effect.” In traditional programming that uses the recipe approach, if a decision-maker or assurance professional wanted to know why a decision was being made, software engineers or analysts could peek inside the program and see that threshold X was reached, which triggered the effect. But, with many machine learning algorithms, it is extremely difficult to look inside an algorithm to ascertain why a certain result was returned.
Recommended citation: Andrew Clark. (2017). “Machine Learning Audits in the ‘Big Data Age’” CIO Insight, 2017.