  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.
