The Banality of (Automated) Evil: Critical Reflections on the Concept of Forbidden Knowledge in Machine Learning Research

Abstract

The development of computer science has raised ethical concerns regarding the potential negative impact of machine learning tools on people and society. In this article, we provide three examples of automated evil: deepfake technology (ab)used by anonymous men to make digitally manipulated pornography to harm women; pattern recognition designed to try to uncover sexual orientation; and deep learning and extensive datasets used by private companies to influence democratic elections. We contend that the concept of ‘forbidden knowledge’ can help to inform a coherent ethical framework in the context of data and computer science research and contribute to tackle automated evil. We conclude that restricting generalised access to extensive data and limiting access to ready-to-use codes would mitigate potential harm caused by machine learning tools. In addition, we advocate that the notions of intersectionality and interdisciplinarity be systematically incorporated in data and computer science research.