Law Commons^™

The Evidentiary Implications Of Interpreting Black-Box Algorithms, Varun Bhatnagar

Northwestern Journal of Technology and Intellectual Property

Biased black-box algorithms have drawn increasing levels of scrutiny from the public. This is especially true for those black-box algorithms with the potential to negatively affect protected or vulnerable populations.1 One type of these black-box algorithms, a neural network, is both opaque and capable of high accuracy. However, neural networks do not provide insights into the relative importance, underlying relationships, structures of the predictors or covariates with the modelled outcomes.2 There are methods to combat a neural network’s lack of transparency: globally or locally interpretable post-hoc explanatory models.3 However, the threat of such measures usually does not bar an actor …