In prediction-based decision-making systems, different perspectives can be at odds: The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly. Balancing these two perspectives is a question of values. We provide a framework to make these value-laden choices clearly visible. For this, we assume that we are given a trained model and want to find decision rules that balance the perspective of the decision maker and of the decision subjects. We provide an approach to formalize both perspectives, i.e., to assess the utility of the decision maker and the fairness towards the decision subjects. In both cases, the idea is to elicit values from decision makers and decision subjects that are then turned into something measurable. For the fairness evaluation, we build on the literature on welfare-based fairness and ask what a fair distribution of utility (or welfare) looks like. In this step, we build on well-known theories of distributive justice. This allows us to derive a fairness score that we then compare to the decision maker's utility for many different decision rules. This way, we provide an approach for balancing the utility of the decision maker and the fairness towards the decision subjects for a prediction-based decision-making system.
翻译:在以预测为基础的决策系统中,不同的观点可能不尽相同:决策者的短期业务目标往往与决策人希望得到公平对待的愿望相冲突。平衡这两个观点是一个价值问题。我们提供了一个框架,使这些价值累累的选择能够明显可见。为此,我们假设我们得到了一个经过培训的模型,希望找到平衡决策者观点和决定主体观点的决策规则。我们提供了一种将这两种观点正规化的方法,即评估决策者的效用和对决策主体的公平性。在这两种情况下,我们的想法是从决策者和决定主体中获取价值观,然后将其转化为可衡量的内容。为了公平性评估,我们利用文献,以基于福利的公平性为基础,并询问公用事业(或福利)的公平分配是什么样子。我们在这一步骤中,我们以众所周知的分化正义理论为基础,从而获得一种公平性评分,然后将决策者的效用与决策者对许多不同决策规则的效用进行比较。这样,我们提供了一种平衡决策人的效用和对决策主体的公正性的方法。