Some claim that AI agents will free workers from the boring parts of their jobs, yet little is known about how workers themselves identify which tasks should be automated. Prior research focuses on occupations, overlooking that workers experience varying levels of meaning across tasks within the same role. We address this gap with a task-level analysis grounded in Graeber's theory of bullshit jobs. Using ratings from 202 workers on 171 workplace tasks, we (1) validate a five-item scale of perceived bullshitness, (2) show that perceived bullshitness strongly predicts desire for AI delegation, and (3) find that such tasks are also seen as requiring less human oversight. Together, these findings suggest that tasks perceived as bullshit are natural candidates for AI delegation, aligning worker preferences with perceived feasibility.

翻译：有人认为，人工智能代理将把工作者从工作中的枯燥部分解放出来，但关于工作者自身如何识别哪些任务应该被自动化，我们知之甚少。以往的研究侧重于职业层面，忽略了在同一角色中，工作者在不同任务上所体验到的意义感存在差异。我们通过一项基于格雷伯“狗屁工作”理论的任务层面分析来填补这一空白。利用202名工作者对171项职场任务的评分，我们（1）验证了一个包含五个条目的感知无意义性量表，（2）显示感知无意义性强烈预测了将任务委托给人工智能的意愿，以及（3）发现此类任务也被认为需要较少的人工监督。综合来看，这些发现表明，被感知为无意义的任务是委托给人工智能的自然候选对象，这使工作者的偏好与感知到的可行性相一致。