Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them

Abstract

The notion of “responsibility gap” with artificial intelligence (AI) was originally introduced in the philosophical debate to indicate the concern that “learning automata” may make more difficult or impossible to attribute moral culpability to persons for untoward events. Building on literature in moral and legal philosophy, and ethics of technology, the paper proposes a broader and more comprehensive analysis of the responsibility gap. The responsibility gap, it is argued, is not one problem but a set of at least four interconnected problems – gaps in culpability, moral and public accountability, active responsibility—caused by different sources, some technical, other organisational, legal, ethical, and societal. Responsibility gaps may also happen with non-learning systems. The paper clarifies which aspect of AI may cause which gap in which form of responsibility, and why each of these gaps matter. It proposes a critical review of partial and non-satisfactory attempts to address the responsibility gap: those which present it as a new and intractable problem (“fatalism”), those which dismiss it as a false problem (“deflationism”), and those which reduce it to only one of its dimensions or sources and/or present it as a problem that can be solved by simply introducing new technical and/or legal tools (“solutionism”). The paper also outlines a more comprehensive approach to address the responsibility gaps with AI in their entirety, based on the idea of designing socio-technical systems for “meaningful human control", that is systems aligned with the relevant human reasons and capacities.

Let Me Take Over: Variable Autonomy for Meaningful Human Control

“…As incidents occur-and sometimes reoccur-the ability to effectively hold the responsible parties answerable for a system's behaviour is essential for maintaining the public's trust to the technology (Knowles and Richards, 2021). Yet, multiple scholars have raised concerns over an ongoing accountability gap, i.e., current moral and legal frameworks fail to explicitly answer who should be held responsible for the actions taken by an autonomous system and how (Raji et al, 2020;Santoni de Sio and Mecacci, 2021). Although the systems themselves cannot be granted legal personhood and held accountable, the organisations and individuals who may be benefiting through their development, deployment, and use can (Bryson et al, 2017;Solaiman, 2017).…”

Time Machines: Artificial Intelligence, Process, and Narrative

“…There may be 'sedimentation' of the technology in the sense that the use of technology itself may recede into the background (Rosenberger and Verbeek, 2017;Lewis, 2021). This phenomenon is well-known since Heidegger and Merleau-Ponty (and later Dreyfus).…”

“…2019; Zuboff, 2019;Danaher, 2019;Couldry & Mejias, 2019;Coeckelbergh, 2020;Véliz, 2020;Berberich et al, 2020;Mohamed et al, 2020;Bartoletti, 2020;Crawford, 2021;Santoni de Sio & Mecacci, 2021;Cowls, 2021). However, less attention has been paid to the philosophical nature of AI.…”

The Ethical Assessment of Autonomous Systems in Practice

“…A further argument in favor of a ban states that since IHL is intended to address human behavior in war, systems capable of selecting and engaging targets independently of a human operator make IHL difficult, if not impossible, to apply. Under the current legal framework, the challenge of holding a human liable for the actions of an AWS is said to create an accountability gap [16], which Santoni de Sio and Mecacci explain is just one form of four responsibility gaps presented by intelligent systems [17]. Proponents of a ban on AWSs highlight that the concept of meaningful human control is not only central to the debate about AWS but that the inability to ensure human control of previous weapons technologies has motivated past disarmament treaties [9].…”