Article

Biases in machine learning models and big data analytics : the international criminal and humanitarian law implications

Nema Milaninia

• International review of the Red Cross, Vol. 102, no. 913, 2020, p. 199-234

English

Advances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. ML is being used for a variety of purposes in the context of international criminal and human rights law. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks. As a result of common human biases to which they are highly susceptible, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.

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• International review of the Red Cross / ICRC
• International review of the Red Cross [Vol. 102, no. 913 (Digital technologies and war), 2020] / ICRC