Ethical Principles for Artificial Intelligence in National Defence

Taddeo, Mariarosaria; McNeish, David; Blanchard, Alexander; Edgar, E.

doi:10.1007/s13347-021-00482-3

Cited by 34 publications

(23 citation statements)

References 37 publications

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“…However questions remain as to the adequacy of current technical solutions, as well as the fact that current XAI efforts are largely driven and funded by military organisations (e.g. DARPA) (Taddeo et al, 2021;Whittaker, 2021). Technical solutions should be supplemented with (co)regulatory efforts for establishing more transparency from digital platforms vis-à-vis independent regulators, especially on matters like internal processes for handling harmful and illegal content through algorithms and AI.…”

Section: Addressing Data Power Asymmetriesmentioning

confidence: 99%

Governing artificial intelligence in the media and communications sector

Pierson¹,

Kerr²,

Robinson³

et al. 2023

Internet Policy Review

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The article analyses critical blindspots in current European Artificial Intelligence (AI) policies and examines the potential impact of data and AI in the emerging socio-technical ecosystem of the contemporary Media and Communications (MC) sector from the perspective of critical media and communication studies. We first identify central blind spots in the dominant EU trustworthy and risk-based approach to governing AI. Next, we propose a novel multi-level framework to analyse key policy challenges for governing AI in the MC sector. The framework and discussion are based on desk research and multi-stakeholder expert discussions. The article concludes with reflections on AI governance in development, deployment and use in the MC sector.Issue 1

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Section: Addressing Data Power Asymmetriesmentioning

confidence: 99%

Governing artificial intelligence in the media and communications sector

Pierson¹,

Kerr²,

Robinson³

et al. 2023

Internet Policy Review

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“…It is important, however, to remark here that the purpose of deployment have been identified as being those directly related to the goal to achieve, i.e. exerting force (Taddeo et al, 2021). Selecting targets and engaging (whether deliberate or dynamic) are directly linked to the purpose of deploying force.…”

Section: A Definition Of Awsmentioning

confidence: 99%

“…Ascribing moral responsibility for the actions performed by AI systems has proved to be extremely problematic in many domains, the case of AWS is not an exception. As argued by (Taddeo et al, 2021 ), whilst a responsibility gap is problematic in all the categories of use of AI within the defence and security domain—namely, sustainment and support, adversarial and non-kinetic, and adversarial and kinetic—the gap is particularly worrying when considering the adversarial and kinetic uses of AI, given the high stakes involved (Sparrow, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

“…From the analysis of the definitions reported in Table 2, four characteristics can be extracted as recurring more often in the reported definitions, namely: autonomy, adapting capabilities, human intervention and control, and purpose of use. While these characteristics point in the right direction when considering what AWS are, for example, they resonate with the definition of AI adopted in (Taddeo, 2019;Taddeo et al, 2021) of a form of autonomous, self-learning agency; the way in which they are described is, at times, conceptually misleading. The next three subsections analyse these characteristics to clarify their implications with respect to the ethical and legal debate on AWS.…”

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confidence: 96%

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A Comparative Analysis of the Definitions of Autonomous Weapons Systems

Taddeo

Blanchard

2022

Sci Eng Ethics

Self Cite

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In this report we focus on the definition of autonomous weapons systems (AWS). We provide a comparative analysis of existing official definitions of AWS as provided by States and international organisations, like ICRC and NATO. The analysis highlights that the definitions draw focus on different aspects of AWS and hence lead to different approaches to address the ethical and legal problems of these weapons systems. This approach is detrimental both in terms of fostering an understanding of AWS and in facilitating agreement around conditions of deployment and regulations of their use and, indeed, whether AWS are to be used at all. We draw from the comparative analysis to identify essential aspects of AWS and then offer a definition that provides a value-neutral ground to address the relevant ethical and legal problems. In particular, we identify four key aspects—autonomy; adapting capabilities of AWS; human control; and purpose of use—as the essential factors to define AWS and which are key when considering the related ethical and legal implications.

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“…Moreover, any answer to this question must provide practical, operational guidance (not mere words). There are important proposals and debates about what ethical AI principles ought to be adopted by defense organizations (e.g., Taddeo et al, 2021), but we focus on how those organizations can operationalize and live up to their ethical AI principles, whatever they might be.…”

Section: The Need For Ethical Interoperabilitymentioning

confidence: 99%

The Challenge of Ethical Interoperability

Danks

Trusilo

2022

DISO

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Defense organizations are increasingly developing ethical principles to guide the design, development, and use of responsible AI, most notably for defense, security, and intelligence uses. While these principles have the potential to lead to more ethical and responsible uses of AI, they also create a novel issue that we term the challenge of ethical interoperability. In particular, we start with the observation that the frequent necessity for collaboration between defense organizations results in their possible need to use one another’s AI systems, either directly or indirectly. In such cases, the AI system presumably satisfies the originator organization’s ethical principles, but the adopting organization should only use it if it also satisfies their AI principles. One might naturally consider using the operating characteristics of the AI system to establish ethical interoperability, as those parallel the features used for technical interoperability. However, we argue that if the operating characteristics are sufficient to enable such assurance, then they will be too detailed to be disclosed. Instead, we propose a system of self-certification that provides adopting organizations with assurances that a system satisfies their ethical AI principles. We argue that our proposed framework properly aligns incentives to ensure ethical interoperability between defense organizations. Moreover, although this challenge is particularly salient for defense organizations, ethical interoperability must also be addressed in non-defense settings by organizations that have developed ethical AI principles.

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Ethical Principles for Artificial Intelligence in National Defence

Cited by 34 publications

References 37 publications

Governing artificial intelligence in the media and communications sector

Governing artificial intelligence in the media and communications sector

A Comparative Analysis of the Definitions of Autonomous Weapons Systems

The Challenge of Ethical Interoperability

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