U.S. strikes against Islamic State (IS) targets in Nigeria on Christmas Day were met with praise from some Donald Trump supporters, who viewed the action as a response to the killings of Christians in the country. The strikes, which took place in Offa, Kwara state, targeted IS militants, according to reports.

Laura Loomer, a far-right political activist, expressed her approval on X, stating, "I can't think of a better way to celebrate Christmas than by avenging the death of Christians through the justified mass killing of Islamic terrorists. You've got to love it! Death to all Islamic terrorists! Thank you." Loomer claimed she was informed by the U.S. defense department, which the Trump administration referred to as the war department, that the strikes were carried out with the Nigerian government.

The U.S. military has been increasing its use of artificial intelligence (AI) in military operations, including target identification and strike coordination. AI algorithms can analyze vast amounts of data from various sources, such as satellite imagery and social media, to identify potential targets and predict enemy movements. This can lead to more precise and efficient strikes, but also raises ethical concerns about the potential for bias and unintended consequences.

The use of AI in military operations is a rapidly evolving field, with new developments emerging constantly. Researchers are working on developing AI systems that can operate autonomously, making decisions without human intervention. This raises concerns about accountability and the potential for errors.

The strikes in Nigeria highlight the complex relationship between technology, politics, and ethics in modern warfare. While some see the strikes as a justified response to terrorism, others raise concerns about the potential for civilian casualties and the long-term consequences of military intervention. The use of AI in these operations further complicates the ethical landscape, raising questions about transparency, accountability, and the potential for bias.