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Last week, states parties met for the first session of the Group of Governmental Experts (GGE) on lethal autonomous weapon systems (LAWS) in 2024. This debate featured the GGE’s most substantive discussion to date about bias under the topic “risk mitigation and confidence building”, including around a working paper dedicated to bias by Canada, Costa Rica, Germany, Ireland, Mexico, and Panama.

In this post, Dr. Ingvild Bode, Associate Professor at the Center for War Studies (University of Southern Denmark) argues that bias is as much a social as a technical problem and that addressing it therefore requires going beyond technical solutions. She holds that the risks of algorithmic bias need to receive more dedicated attention as the GGE’s work turns towards thinking around operationalisation. These arguments are based on the author’s presentation at the GGE side event “Fixing Gender Glitches in Military AI: Mitigating Unintended Biases and Tackling Risks” organised by UNIDIR on 6 March 2024.

Falling under the radar: algorithmic bias and military applications of AI

Algorithmic bias has long been recognized as a key problem affecting decision-making processes that integrate artificial intelligence (AI) technologies. The increased use of AI in making military decisions relevant to the use of force has sustained such questions about biases in these technologies and in how human users programme with and rely on data based on hierarchized socio-cultural norms, knowledges, and modes of attention.

In this post, Dr Ingvild Bode, Professor at the Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD researcher at the “Meaningful Human Control: Between Regulation and Reflexion” project, Paderborn University, unpack the problem of algorithmic bias with reference to AI-based decision support systems (AI DSS). They examine three categories of algorithmic bias – preexisting bias, technical bias, and emergent bias – across four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing discussion about AI in the military domain should consider the impact of algorithmic bias on AI DSS more seriously.

The problem of algorithmic bias in AI-based military decision support systems

Over the past decade, discussions surrounding artificial intelligence (AI) in the military domain have largely focused on autonomous weapon systems. This is partially due to the ongoing debates of the Group of Governmental Experts on Lethal Autonomous Weapons Systems of the Convention on Certain Conventional Weapons. While autonomous weapon systems are indeed a pressing concern, the critical reality is that AI is hastily deployed to gather intelligence and, even more worrisome, to support militaries to select and engage targets.

As AI-based decision support systems (AI DSS) are increasingly used in contemporary battlefields, Jimena Sofía Viveros Álvarez, member of the United Nations Secretary General’s High-Level Advisory Body on AI, REAIM Commissioner and OECD.AI Expert, advocates against the reliance on these technologies in supporting the target identification, selection and engagement cycle as their risks and inefficacies are a permanent fact which cannot be ignored, for they actually risk exacerbating civilian suffering.

The risks and inefficacies of AI systems in military targeting support

The military decision-making process is facing a challenge by the increasing number of interconnected sensors capturing information on the battlefield. The abundance of information offers advantages for operational planning – if it can be processed and acted upon rapidly. This is where AI-assisted decision-support systems (DSS) enter the picture. They are meant to empower military commanders to make faster and more informed decisions, thus accelerating and improving the decision-making process. Although they are just meant to assist – and not replace – human decision-makers, they pose several ethical challenges which need to be addressed.

In this post, Matthias Klaus, who has a background in AI ethics, risk analysis and international security studies, explores the ethical challenges associated with a military AI application often overshadowed by the largely dominating concern about autonomous weapon systems (AWS). He highlights a number of ethical challenges associated specifically with DSS, which are often portrayed as bringing more objectivity, effectivity and efficiency to military decision-making. However, they could foster forms of bias, infringe upon human autonomy and dignity, and effectively undermine military moral responsibility by resulting in peer pressure and deskilling.

Transcending weapon systems: the ethical challenges of AI in military decision support systems

The International Committee of the Red Cross (ICRC) Humanitarian Law & Policy blog is a unique space for timely analysis and debate on international humanitarian law (IHL) issues and the policies that shape humanitarian action.

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