Algorithmic Recourse aims to provide actionable explanations, or recourse
plans, to overturn potentially unfavourable decisions taken by automated
machine learning models. In this paper, we propose an interaction paradigm
based on a guided interaction pattern aimed at both eliciting the users'
preferences and heading them toward effective recourse interventions. In a
fictional task of money lending, we compare this approach with an exploratory
interaction pattern based on a combination of alternative plans and the
possibility of freely changing the configurations by the users themselves. Our
results suggest that users may recognize that the guided interaction paradigm
improves efficiency. However, they also feel less freedom to experiment with
"what-if" scenarios. Nevertheless, the time spent on the purely exploratory
interface tends to be perceived as a lack of efficiency, which reduces
attractiveness, perspicuity, and dependability. Conversely, for the guided
interface, more time on the interface seems to increase its attractiveness,
perspicuity, and dependability while not impacting the perceived efficiency.
That might suggest that this type of interfaces should combine these two
approaches by trying to support exploratory behavior while gently pushing
toward a guided effective solution.

算法性回溯的研究目标在于为自动化机器学习模型所做出的可能不利决策提供可行性解释或改变方案。本文提出了一种基于引导互动模式的交互范式，旨在引导用户表达偏好并引导他们采取有效的改变方案。作者通过在虚构的借贷任务中进行对比，将该方法与基于替代方案和用户自行配置的自由性的探索互动模式进行比较。结果显示，用户可能认为引导互动范式提高了效率，但也感到在 “假设” 场景下的自由度较低。然而，对于纯探索界面所花费的时间通常被视为缺乏效率，从而降低了其吸引力、明确性和可靠性。相反，对于引导界面，时间的增加似乎提高了其吸引力、明确性和可靠性，而不会影响到用户对其效率的感知。这可能表明这种类型的界面应该将这两种方法结合起来，既支持探索行为，又慢慢引导朝着有效解决方案的方向前进。