Black box algorithms and the rights of individuals: No easy solution to the "explainability" problem

Over the last few years, the interpretability of classification models has been a very active area of research. Recently, the concept of interpretability was given a more specific legal context. In 2016, the EU adopted the General Data Protection Regulation (GDPR), containing the right to explanation for people subjected to automated decision-making (ADM). The regulation itself is very reticent about what such a right might imply. As a result, since the introduction of the GDPR there has been an ongoing discussion about not only the need to introduce such a right, but also about its scope and practical consequences in the digital world. While there is no doubt that the right to explanation may be very difficult to implement due to technical challenges, any difficulty in explaining how algorithms work cannot be considered a sufficient reason to completely abandon this legal safeguard. The aim of this article is twofold. First, to demonstrate that the interpretability of 'black box' machine learning algorithms is a challenging technical problem for which no solutions have been found. Second, to demonstrate how the explanation task should instead be completed using well-known and well-trialled IT solutions, such as event logging or statistical analysis of the algorithm. Based on the evidence exposed in this paper, the authors find that the most effective solution would be to benchmark the automated decision-making algorithms using certification frameworks, thus balancing the need to ensure adequate protection of individuals' rights with the understandable expectations of AI technology providers to have their intellectual property rights protected.