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：许多高风险的决策遵循专家参与式结构，即人类操作者接收来自算法的建议，但最终决策由人类做出。因此，算法的建议可能与实践中实际实施的决策存在差异。然而，大多数算法建议是通过求解一个假设建议会被完美实施的优化问题获得的。我们提出了一种采纳感知型优化框架，以捕捉推荐策略与实施策略之间的二元性，并分析部分采纳对最优建议的影响。我们证明，忽视部分采纳现象（正如当前大多数推荐引擎的做法）可能会导致性能恶化到任意严重程度，无论是与当前人类基线性能相比，还是与推荐算法预期的性能相比。我们的框架还提供了有用的工具来分析最优推荐策略的结构并计算这些策略，这些策略天然免疫于此类人类偏离行为，并能保证在基线策略基础上有所改进。