Gender and Lethal AutonomousWeapons Systems
Shimona Mohan and Dongyoun Cho

What Are LAWS?

While there is no agreed definitionof Lethal Autonomous Weapons
Systems (LAWS) yet, LAWS are
broadly understood to refer toweapon systems that are designed
to select and engage one or moretargets without the need for humanintervention after activation.

Autonomy in LAWS can be achievedin different ways, and rapid advancesin the field of artificial intelligence (AI)and machine learning are setting
the scene for a new generationof autonomous weapons systems
in scale and scope.

The Convention on Certain
Conventional Weapons Group ofGovernmental Experts on LAWS(CCW GGE on LAWS) released
an overview of characterizationsof these systems.

Gender Matters in LAWS

Male-Dominated TechWorkspaces: 92% ofsoftware professionals
worldwide are men.

Militarised Masculinities:
Women form a minority of military
personnel in most countries,
ranging from 0.3% to 20%.

Gender Inequality in Diplomacy and33%
Policy-making: Women represent only 1/3of diplomats participating in arms control anddisarmament meetings such as CCW GGE on LAWS.

92%

Biased Systems: A 2021 review of 133 AI systems
employed across sectors shows that 44% exhibit
gender bias and 26% exhibit both gender and racialbiases.

The Problem of Bias
Societal norms can be implicitly encoded in LAWS, which can
result in biases at the systems’ output stage based on gender,
race, age, and other demographic qualifiers.
Bias risks operational efficiency of AI systems and forms
a pertinent issue in discussions around LAWS.
Biases in AI are usually a result of any combination of
3 oversights:

Potential consequences of gender biases in military AI
systems like LAWS can be assessed from civilian examplesof bias, such as:••

1 Incorrect, incomplete or unrepresentative datasets;

Popular facial recognition technologies do not detectcertain faces due to their gender or race;
Women were not shortlisted for jobs due to AI-based
recruitment tools passing over women candidates infavour of men;
Medical algorithms have been known to deny care toindividuals based on gender, race and age.

2 Machine learning models which introduce, reinforce
or exacerbate bias in the data; and•3 Human developers and operators who may encode theirown biases or overlook pre-existing biases in the data,processing or output of the systems.

Gender biases in LAWS and related systems may resultin misidentifying women as non-human objects or
miscategorizing civilian men as combatants.

Evidence of bias in civilian applications of AI is easy to find.
However, less research exists on how military applicationsof AI may reproduce inequalities.

There is no neutral category of a “human”, hence military
applications of AI need to be transparent about how thesesystems respond to and reflect the diversity of humanity.

Gender in the CCW GGE on LAWS
The CCW GGE on LAWS is the sole intergovernmental forum where LAWS are
discussed, and gender considerations have been addressed in various ways:
Initial discussions at the GGE (2017 & 2018) mentioned
the importance of including gender perspectives
and striving towards gender balance amongst thedelegations at the CCW.
From 2019 onwards, the issue of biases, including
gender and racial bias, in data-sets and algorithms
became a point of discussion in the context of ethics andoperational efficiency of LAWS.
Starting from 2021, gender biases were discussed
as a subset of risk mitigation, which forms one of the
11 Guiding Principles on LAWS agreed upon by all
High Contracting Parties to the CCW by consensus.

In 2022 and 2023,
gender biases
remained on
the agenda as aconsideration under ethics, legal reviews, risk mitigationand confidence-building measures, but were missing
from the final documents produced by the GGE.
In 2024, a group of Member States released a working
paper highlighting ways to address biases in autonomous
weapons, an ongoing effort at the GGE that UNIDIRsupported through a side event on fixing gender glitches
in military AI.

Areas for Action
Technical Measures:
Identify and address biases in data and algorithms,
Fund representative and gender-disaggregated datasets,
Audit military AI systems to ensure bias mitigation.

Gender Parity:
Ensure meaningful representation of women diplomats
as well as gender experts at all stages of policymaking
concerning LAWS.

Gender-Sensitization Training:
Provide gender-sensitization training as part of
the technical training to all personnel involved in
the design, development and deployment of LAWS.

Research and Knowledge-Creation:
Fund and support research to comprehend the evolving
gender-specific considerations on LAWS and utilize
the findings to guide gender-sensitive policies.

Gender Reviews:

International Cooperation:
Encourage international multistakeholder collaboration
to tackle gender issues in AI and LAWS, especiallyby exchanging knowledge and best practices.

Conduct gender reviews of new and emerging
military technologies like LAWS under regularnational legal reviews of weapons systems.

Capacity Building:Support capacity building initiatives to enhance knowledge and expertise in gender considerations on LAWSand military AI, such as the UNIDIR Women in AI Fellowship.

To learn more about UNIDIR, visit unidir.org