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Many high-stake decisions follow an expert-in-loop structure in that a humanoperator receives recommendations from an algorithm but is the ultimatedecision maker. Hence, the algorithm's recommendation may differ from theactual decision implemented in practice. However, most algorithmicrecommendations are obtained by solving an optimization problem that assumesrecommendations will be perfectly implemented. We propose an adherence-awareoptimization framework to capture the dichotomy between the recommended and theimplemented policy and analyze the impact of partial adherence on the optimalrecommendation. We show that overlooking the partial adherence phenomenon, asis currently being done by most recommendation engines, can lead to arbitrarilysevere performance deterioration, compared with both the current human baselineperformance and what is expected by the recommendation algorithm. Our frameworkalso provides useful tools to analyze the structure and to compute optimalrecommendation policies that are naturally immune against such humandeviations, and are guaranteed to improve upon the baseline policy.

The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations