Understanding the Big Mind. Does the GDPR Bridge the Human-Machine Intelligibility Gap?

Abstract

The article aims to highlight the main legal challenge posed by the increasing implementation of algorithmic credit scoring models, namely the right to a “meaningful information about the logic involved” in solely automated decisions, as enshrined in Article 22 of the General Data Protection Regulation. Following a brief overview of the relevant computer science features of Big Data and Machine Learning applications, the analysis proceeds by first maintaining the existence of a right to an explanation, then discussing: (i) the transparency fallacy of ML systems; (ii) the possible clash with Trade Secret and Copyright; and (iii) possible legal strategies to ensure effective and useful intelligibility of automated decision-making processes. The ultimate objective is to raise public interest in the subtly spreading privacy threats posed by the use of artificial intelligence software underpinned by Big Data streamflow of personal information to take decisions that significantly affect the legal sphere of data subjects.

Keywords: GDPR, Artificial Intelligence, Big Data, creditworthiness, Intellectual Property, Transparency, Machine Learning