By some estimates, [a coalition between Saudi Arabia and eight other Sunni Arab states has] carried out over 20,000 air strikes, many of which have killed Yemeni civilians and destroyed their property, allegedly in direct violation of international law. Human rights organizations have since sought to document such war crimes in an effort to stop them through legal challenges. But the gold standard, on-the-ground verification by journalists and activists, is often too dangerous to be possible. Instead, organizations have increasingly turned to crowdsourced mobile photos and videos to understand the conflict, and have begun submitting them to court to supplement eyewitness evidence.
But as digital documentation of war scenes has proliferated, the time it takes to analyze it has exploded…Now an initiative that will soon mount a challenge in the UK court system is trialing a machine-learning alternative. It could model a way to make crowdsourced evidence more accessible and help human rights organizations tap into richer sources of information.
“[I]f if you can show hundreds of videos of hundreds of incidents of hospitals being targeted, you can see that this is really a deliberate strategy of war. When things are seen as deliberate, it becomes more possible to identify intent. And intent might be something useful for legal cases in terms of accountability for war crimes,” [researcher Jeff Deutch said.]