People affected by machine learning model decisions may benefit greatly from access to recourses, i.e. suggestions about what features they could change to receive a more favorable decision from the model. Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function. This is an unrealistic assumption because users might have diverse preferences about their willingness to change certain features. In this work, we introduce a new method for identifying recourse sets for users which does not assume that users' preferences are known in advance. We propose an objective function, Expected Minimum Cost (EMC), based on two key ideas: (1) when presenting a set of options to a user, there only needs to be one low-cost solution that 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 from a distribution, then finding a recourse set that achieves a good cost for these samples. 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 users demonstrates that our method satisfies up to 25.89 percentage points more users compared to strong baseline methods, while, the human evaluation shows that our recourses are preferred more than twice as often as the strongest baseline recourses. Finally, using standard fairness metrics we show that our method can provide more fair solutions across demographic groups than baselines. We provide our source code at: https://github.com/prateeky2806/EMC-COLS-recourse
翻译:受机器学习模式决定影响的人可能从利用追索手段中受益匪浅,即建议他们可以改变哪些特点,以便从模型中获得更有利的决定。当前的做法试图优化用户在采用追索手段时发生的成本,但他们假设所有用户都具有相同的成本功能。这是一个不现实的假设,因为用户可能对其改变某些特征的意愿有不同的偏好。在这项工作中,我们引入一种新的方法,为用户确定追索机制,而用户并不预先知道用户的偏好。我们基于以下两个关键想法提出了一个客观功能,即预期最低成本(EMC ),即: (1) 当向用户展示一套公平选项时,只需优化用户在采用一个低成本的解决方案; (2) 当我们不知道用户的真实成本功能时,我们就可以通过首先从分配中抽样比较合理的成本功能来优化用户的满意度,然后找到一套能够为这些样本带来良好成本的追索集。 我们以新的最接近的离心最优化算法,即成本奥基化本地搜索(OCLS),这可以保证改进追索方法的质量,而不是向用户提出公平选择的公平选择,只需要一种低价计算方法,而我们更精确地评估。