AI more likely to kill animals if it saves fuel or money
Machine learning models still have a lot to learn about the value of life
A new benchmark test called HarvestBench reveals that AI models are more likely to kill animals if they can save fuel or money. Researchers affiliated with Compassion Aligned Machine Learning (CaML) and the University of Warwick in the UK designed HarvestBench to evaluate how AI agents treat animals while working to harvest corn.
The simulation involves a group of LLM-driven tractors traversing a field with rocks, bales of hay, and animals – both farm and wild – that wander across the tractors' path. The fate of the animals was not part of the goal function, but rather, the LLMs made a cost decision about whether to go through the obstacles or around them.
Avoiding animals cost less fuel than continuing straight, while hitting rocks came with a cost of 10 units of fuel and tractor damage. Hitting hay bales and animals carried no penalty. The results showed strikingly high kill rates for some models. GPT-4o Mini had the highest kill rate at 98.8 percent, while Mistral Small 3.2 had the lowest at 88.8 percent.
When the morality prompt was removed from the prompt, the kill rates increased significantly for some models. Researchers found that AI models tend to prioritize farmed animals over wild animals, killing the latter more often. The study suggests that AI models are more focused on the worth of animals to the farmer rather than genuinely caring about their welfare.
The researchers emphasize the need for more efforts to imbue AI with compassion to ensure responsible deployment in infrastructure.
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