Interactive AI systems increasingly employ a human-in-the-loop strategy. This creates new challenges for the HCI community when designing such systems. We reveal and investigate some of these challenges in a case study with an industry partner, and developed a prototype human-in-the-loop system for preference-guided 3D model processing. Two 3D artists used it in their daily work for 3 months. We found that the human-AI loop often did not converge towards a satisfactory result and designed a lab study (N=20) to investigate this further. We analyze interaction data and user feedback through the lens of theories of human judgment to explain the observed human-in-the-loop failures with two key insights: 1) optimization using preferential choices lacks mechanisms to deal with inconsistent and contradictory human judgments; 2) machine outcomes, in turn, influence future user inputs via heuristic biases and loss aversion. To mitigate these problems, we propose descriptive UI design guidelines. Our case study draws attention to challenging and practically relevant imperfections in human-AI loops that need to be considered when designing human-in-the-loop systems.
翻译:互动的人工智能系统越来越多地采用 " 流动中人 " 战略。这给HCI社区在设计这种系统时带来了新的挑战。我们在与一个行业伙伴进行的案例研究中披露和调查其中一些挑战,并开发了特惠制3D模型处理的原型 " 流动中人 " 系统。两名3D艺术家在其日常工作中使用了该系统,为期3个月。我们发现,人-AI循环往往没有达到令人满意的结果,并设计了一个实验室研究(N=20)来进一步调查这一问题。我们通过人类判断理论的透镜分析互动数据和用户反馈,以解释观察到的 " 流动中人 " 失败,其中有两个关键见解:(1) 利用优惠选择优化缺乏处理不一致和矛盾的人类判断的机制;(2) 机器结果反过来通过超自然偏见和损失转换影响未来的用户投入。为解决这些问题,我们提出了描述性UI设计准则。我们的案例研究提请人们注意在设计人类-AI循环中具有挑战性和实际相关的不完善之处,在设计“流动中”时需要考虑。