The problem of identifying algorithmic recourse for people affected by machine learning model decisions has received much attention recently. Some recent works model user-incurred cost, which is directly linked to user satisfaction. But they assume a single global cost function that is shared across all users. This is an unrealistic assumption when users have dissimilar preferences about their willingness to act upon a feature and different costs associated with changing that feature. In this work, we formalize the notion of user-specific cost functions and introduce a new method for identifying actionable recourses for users. By default, we assume that users' cost functions are hidden from the recourse method, though our framework allows users to partially or completely specify their preferences or cost function. We propose an objective function, Expected Minimum Cost (EMC), based on two key ideas: (1) when presenting a set of options to a user, it is vital that there is at least one low-cost solution the user could adopt; (2) when we do not know the user's true cost function, we can approximately optimize for user satisfaction by first sampling plausible cost functions, then finding a set that achieves a good cost for the user in expectation. We optimize EMC with a novel discrete optimization algorithm, Cost-Optimized Local Search (COLS), which is guaranteed to improve the recourse set quality over iterations. Experimental evaluation on popular real-world datasets with simulated user costs demonstrates that our method satisfies up to 25.89 percentage points more users compared to strong baseline methods. Using standard fairness metrics, we also show that our method can provide more fair solutions across demographic groups than comparable methods, and we verify that our method is robust to misspecification of the cost function distribution.
翻译:确定受机器学习模式决定影响的人的算法追索方法的问题最近引起了很大的注意。最近的一些工作模式是用户成本的模型,它直接与用户的满意度有关。但他们承担了一个单一的全球成本功能,所有用户都共享。这是一个不切实际的假设,因为用户对于是否愿意对与该特征变化相关的特征和不同成本采取行动有不同的偏好。在这项工作中,我们正式确定用户特定成本功能的概念,并采用新的方法为用户确定可操作的追索方法。默认情况下,我们假设用户的成本功能隐藏在追索方法中,尽管我们的框架允许用户部分或全部指定其偏好或成本功能。我们基于两个关键想法提出一个客观的函数,即预期最低成本(EMC),即用户向用户提出一套不同的选项,用户可以采用至少一个低成本的解决方案;当我们不知道用户的真正成本功能时,我们可以通过首先抽样真实的成本功能来优化用户的满意度,然后找到一套为用户带来良好预期成本的组合。我们用一个比真实的用户成本成本分析,我们用一种新的方法来优化EMC,通过一种保证的精确的模型分析方法来显示我们的标准成本质量,我们用来显示我们比成本的精确的计算方法,我们用来显示我们比成本的精确的计算方法,我们更精确的精确的计算方法比它比成本。我们用来显示我们用来显示我们的用户的精确的精确的计算方法比的精确的方法。