Innovation-Proof Global Governance for Military Artificial Intelligence?

2019 ◽  
Vol 10 (1) ◽  
pp. 129-157 ◽  
Author(s):  
Matthijs M Maas
Keyword(s):  
Artificial Intelligence ◽  
Global Governance ◽  
State Of The Art ◽  
Arms Races ◽  
International Debate ◽  
Human Control ◽  
Future Developments ◽  
Autonomous Weapons ◽  
Governance Strategies ◽  
Legal Frameworks

Amidst fears over artificial intelligence ‘arms races’, much of the international debate on governing military uses of AI is still focused on preventing the use of lethal autonomous weapons systems (laws). Yet ‘killer robots’ hardly exhaust the potentially problematic capabilities that innovation in military AI (mai) is set to unlock. Governance initiatives narrowly focused on preserving ‘meaningful human control’ over laws therefore risk being bypassed by the technological state-of-the-art. This paper departs from the question: how can we formulate ‘innovation-proof governance’ approaches that are resilient or adaptive to future developments in military AI? I develop a typology for the ways in which mai innovation can disrupt existing international legal frameworks. This includes ‘direct’ disruption – as new types of mai capabilities elude categorization under existing regimes – as well as ‘indirect’ disruption, where new capabilities shift the risk landscape of military AI, or change the incentives or values of the states developing them. After discussing two potential objections to ‘innovation-proof governance’, I explore the advantages and shortcomings of three possible approaches to innovation-proof governance for military AI. While no definitive blueprint is offered, I suggest key considerations for governance strategies that seek to ensure that military AI remains lawful, ethical, stabilizing, and safe.

Autonomous Weapons and the Ethics of Artificial Intelligence

2020 ◽  
pp. 212-236
Author(s):  
Peter Asaro
Keyword(s):  
Artificial Intelligence ◽  
Autonomous System ◽  
Human Control ◽  
Moral Agents ◽  
Lethal Force ◽  
Autonomous Weapons ◽  
Weapons Systems

As the militaries of technologically advanced nations seek to apply increasingly sophisticated AI and automation to weapons technologies, a host of ethical, legal, social, and political questions arise. Central among these is whether it is ethical to delegate the decision to use lethal force to an autonomous system that is not under meaningful human control. Further questions arise as to who or what could or should be held responsible when lethal force is used improperly by such systems. This chapter argues that current autonomous weapons are not legal or moral agents that can be held morally responsible or legally accountable for their choices and actions, and that therefore humans need to maintain control over such weapons systems.

scholarly journals Autonomous Weapons Systems, Artificial Intelligence, and the Problem of Meaningful Human Control

2021 ◽  
Vol 5 (1) ◽  
pp. 53-72
Author(s):  
Elke Schwarz
Keyword(s):  
Artificial Intelligence ◽  
Moral Agency ◽  
Human Control ◽  
Autonomous Weapons ◽  
Weapons Systems

In this article, I explore the (im)possibility of human control and question the presupposition that we can be morally adequately or meaningfully in control over AI-supported LAWS. Taking seriously Wiener’s warning that “machines can and do transcend some of the limitations of their designers and that in doing so they may be both effective and dangerous,” I argue that in the LAWS human-machine complex, technological features and the underlying logic of the AI system progressively close the spaces and limit the capacities required for human moral agency.

scholarly journals The Design of Human Oversight in Autonomous Weapon Systems

2019 ◽  
Author(s):  
Ilse Verdiesen
Keyword(s):  
Artificial Intelligence ◽  
Emerging Technology ◽  
Unintended Consequences ◽  
Strict Sense ◽  
Weapon Systems ◽  
Human Control ◽  
Autonomous Weapons ◽  
Technical Architecture ◽  
Definition Of ◽  
Weapons Systems

Autonomous Weapon Systems (AWS) can be defined as weapons systems equipped with Artificial Intelligence (AI). They are an emerging technology and are increasingly deployed on the battlefield. In the societal debate on Autonomous Weapon Systems, the concept of Meaningful Human Control (MHC) is often mentioned as requirement, but MHC will not suffice as requirement to minimize unintended consequences of Autonomous Weapon Systems, because the definition of ‘control’ implies that one has the power to influence or direct the course of events or the ability to manage a machine. The characteristics autonomy, interactivity and adaptability of AI  in Autonomous Weapon Systems inherently imply that control in strict sense is not possible. Therefore, a different approach is needed to minimize unintended consequences of AWS. Several scholars are describing the concept of Human Oversight in Autonomous Weapon Systems and AI in general. Just recently Taddeo and Floridi (2018) describe that human oversight procedures are necessary to minimize unintended consequences and to compensate unfair impacts of AI. In my PhD project, I will analyse the concepts that are needed to define, model, evaluate and ensure human oversight in Autonomous Weapons and design a technical architecture to implement this.

scholarly journals Autonomous Weapons Systems and Meaningful Human Control: Ethical and Legal Issues

2020 ◽  
Vol 1 (4) ◽  
pp. 187-194
Author(s):  
Daniele Amoroso ◽  
Guglielmo Tamburrini
Keyword(s):  
Artificial Intelligence ◽  
Research Community ◽  
Legal Issues ◽  
Shared Control ◽  
Human Intervention ◽  
Human Control ◽  
Autonomous Weapons ◽  
Responsibility Ascription ◽  
Ethical And Legal Issues ◽  
Weapons Systems

Abstract Purpose of Review To provide readers with a compact account of ongoing academic and diplomatic debates about autonomy in weapons systems, that is, about the moral and legal acceptability of letting a robotic system to unleash destructive force in warfare and take attendant life-or-death decisions without any human intervention. Recent Findings A précis of current debates is provided, which focuses on the requirement that all weapons systems, including autonomous ones, should remain under meaningful human control (MHC) in order to be ethically acceptable and lawfully employed. Main approaches to MHC are described and briefly analyzed, distinguishing between uniform, differentiated, and prudential policies for human control on weapons systems. Summary The review highlights the crucial role played by the robotics research community to start ethical and legal debates about autonomy in weapons systems. A concise overview is provided of the main concerns emerging in those early debates: respect of the laws of war, responsibility ascription issues, violation of the human dignity of potential victims of autonomous weapons systems, and increased risks for global stability. It is pointed out that these various concerns have been jointly taken to support the idea that all weapons systems, including autonomous ones, should remain under meaningful human control (MHC). Main approaches to MHC are described and briefly analyzed. Finally, it is emphasized that the MHC idea looms large on shared control policies to adopt in other ethically and legally sensitive application domains for robotics and artificial intelligence.

scholarly journals Coupling levels of abstraction in understanding meaningful human control of autonomous weapons: a two-tiered approach

2021 ◽  
Author(s):  
Steven Umbrello
Keyword(s):  
Target Selection ◽  
Military Operations ◽  
Levels Of Abstraction ◽  
International Debate ◽  
Design Ethics ◽  
Weapon Systems ◽  
Human Control ◽  
Autonomous Weapons ◽  
Military Planning ◽  
Design Requirements

AbstractThe international debate on the ethics and legality of autonomous weapon systems (AWS), along with the call for a ban, primarily focus on the nebulous concept of fully autonomous AWS. These are AWS capable of target selection and engagement absent human supervision or control. This paper argues that such a conception of autonomy is divorced from both military planning and decision-making operations; it also ignores the design requirements that govern AWS engineering and the subsequent tracking and tracing of moral responsibility. To show how military operations can be coupled with design ethics, this paper marries two different kinds of meaningful human control (MHC) termed levels of abstraction. Under this two-tiered understanding of MHC, the contentious notion of ‘full’ autonomy becomes unproblematic.

Understanding AI and Autonomy: Problematizing the Meaningful Human Control Argument against Killer Robots

2021 ◽  
pp. 41-56
Author(s):  
Tim McFarland ◽  
Jai Galliott
Keyword(s):  
Precise Definition ◽  
International Humanitarian Law ◽  
Humanitarian Law ◽  
International Debate ◽  
Human Control ◽  
Lethal Force ◽  
Autonomous Weapons ◽  
Weapons Systems

The physical and temporal removal of the human from the decision to use lethal force underpins many of the arguments against the development of autonomous weapons systems. In response to these concerns, Meaningful Human Control has risen to prominence as a framing concept in the ongoing international debate. This chapter demonstrates how, in addition to the lack of a universally accepted precise definition, reliance on Meaningful Human Control is conceptually flawed. Overall, this chapter analyzes, problematizes, and explores the nebulous concept of Meaningful Human Control, and in doing so demonstrates that it relies on the mistaken premise that the development of autonomous capabilities in weapons systems constitutes a lack of human control that somehow presents an insurmountable challenge to existing International Humanitarian Law.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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