Your AI Travel Agent Would Book You a Bullfight: An Agentic Benchmark for Implicit Animal Welfare in Frontier AI Models
Previous research has evaluated animal welfare using question-and-answer benchmarks. This study investigates whether these evaluations also hold in agentic settings. The agents may showcase different behaviors compared to stand-alone large language models, as demonstrated in prior studies. This work introduces \textit{TAC (Travel Agent Compassion)}: the first agentic benchmark for assessing animal exploitation. TAC evaluates AI agentic behavior in travel booking scenarios across six animal categories, using thirteen hand-authored scenarios that vary by price, rating, and position, expanded via four augmentation variants into $52$ prompts and run for three epochs, giving $156$ scored obser...
| Authors | Jasmine Brazilek, Joel Christoph, Maheep Chaudhary et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 June 2026 |
| Last updated at source | 6 July 2026 |
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- CompassioninMachineLearning/tacDataset · cited by
- considersentience/bearclaw-benchmarkDataset · cited by
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