Entanglement

Machine learning and human ethics in driver-less car crashes

Abstract

This paper is based on driver-less car technology as currently being developed by Google and Tesla, two companies that amplify their work in the media. More specifically, I focus on the moment of real and imagined crashes involving driver-less cars, and argue that the narrative of ‘ethics of driver-less cars’ indicates a shift in the construction of ethics, as an outcome of machine learning rather than a framework of values. Through applications of the ‘Trolley Problem’, among other tests, ethics has been transformed into a valuation based on processing of big data. Thus ethics-as-software enables what I refer to as big data-driven accountability. In this formulation, ‘accountability’ is distinguished from ‘responsibility’; responsibility implies intentionality and can only be assigned to humans, whereas accountability includes a wide net of actors and interactions (in Simon). ‘Transparency’ is one of the more established, widely acknowledged mechanisms for accountability; based on the belief that seeing into a system delivers the truth of that system and thereby a means to govern it. There are however limitations to this mechanism in the context of algorithmic transparency (Ananny and Crawford).