Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm's recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations are obtained by solving an optimization problem that assumes recommendations will be perfectly implemented. We propose an adherence-aware optimization framework to capture the dichotomy between the recommended and the implemented policy and analyze the impact of partial adherence on the optimal recommendation. We show that overlooking the partial adherence phenomenon, as is currently being done by most recommendation engines, can lead to arbitrarily severe performance deterioration, compared with both the current human baseline performance and what is expected by the recommendation algorithm. Our framework also provides useful tools to analyze the structure and to compute optimal recommendation policies that are naturally immune against such human deviations, and are guaranteed to improve upon the baseline policy.

翻译：许多高风险决策遵循专家参与结构，即人类操作者接收来自算法的建议，但作为最终决策者。因此，算法的建议可能与实践中实际执行的决策存在差异。然而，大多数算法建议是通过求解一个假设建议将被完美执行的优化问题而获得的。我们提出了一种依从感知优化框架，以捕捉建议策略与实施策略之间的二分性，并分析部分依从对最优建议的影响。我们表明，忽视部分依从现象（正如目前大多数推荐引擎所做的那样）可能导致与当前人类基准性能及推荐算法预期性能相比，任意严重的性能恶化。我们的框架还提供了有用的工具，用于分析最优推荐策略的结构并计算这些策略，这些策略自然免疫于此类人类偏差，并保证在基线策略基础上有所改进。