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 add
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.
Executive Summary
The article 'Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them' offers a comprehensive analysis of the responsibility gap in the context of artificial intelligence. The authors propose a broader understanding of the responsibility gap, identifying four interconnected problems: gaps in culpability, moral and public accountability, and active responsibility. They critique existing attempts to address the responsibility gap, arguing that they are often limited or unsatisfactory. Instead, the authors propose a more comprehensive approach, focusing on designing socio-technical systems for 'meaningful human control.' This approach prioritizes systems that are aligned with human reasons and capacities, aiming to address the responsibility gaps in their entirety. The article contributes significantly to the ongoing debate on the ethics of AI and the importance of responsibility in the development and deployment of these systems.
Key Points
- ▸ The responsibility gap is a set of interconnected problems, rather than a single issue.
- ▸ Four distinct responsibility gaps are identified: culpability, moral and public accountability, and active responsibility.
- ▸ Existing attempts to address the responsibility gap are often limited or unsatisfactory, and a more comprehensive approach is needed.
Merits
Comprehensive Analysis
The article provides a thorough and nuanced examination of the responsibility gap, identifying multiple dimensions and sources.
Theoretical Contribution
The authors build on existing literature in moral and legal philosophy, ethics of technology, and make a significant theoretical contribution to the field.
Practical Implications
The article provides concrete recommendations for designing socio-technical systems that prioritize 'meaningful human control.'
Demerits
Complexity
The article's comprehensive scope and multiple responsibility gaps may make it challenging for readers to follow or fully grasp the authors' arguments.
Limited Empirical Evidence
The article relies on theoretical analysis and literature review, but does not provide empirical evidence to support its claims or illustrate the practical application of its recommendations.
Expert Commentary
The article's comprehensive analysis of the responsibility gap and its multiple dimensions is a significant contribution to the field of AI ethics. The authors' proposal for designing socio-technical systems that prioritize 'meaningful human control' is a timely and important recommendation. However, the article's focus on theoretical analysis and literature review may limit its practical impact and application. To fully realize the implications of this work, empirical research and case studies are needed to demonstrate the effectiveness of these recommendations in real-world contexts. Furthermore, the article's critique of existing attempts to address the responsibility gap highlights the need for a more nuanced and multidisciplinary approach to addressing the challenges posed by AI systems.
Recommendations
- ✓ Develop and deploy AI systems that prioritize 'meaningful human control' and are designed to address the responsibility gaps identified in this article.
- ✓ Conduct empirical research and case studies to demonstrate the effectiveness of these recommendations in real-world contexts.
