On preferring people to algorithms
基于六国全国代表性样本，研究人们在风险情境下对算法与人类决策者的偏好，发现多数人偏好人类，提供算法信息可显著降低这一偏好，但信息干预总体效果有限。
Abstract This study explores preferences for algorithmic or human decision-making across six countries using nationally representative samples. Participants evaluated ten decision scenarios, typically involving serious risks of one or another kind, in which they choose between algorithmic or human decision-makers under varying informational conditions: baseline (no additional information), brief information about the expertise of the human decision-maker, brief information about the algorithm’s data-driven foundation, and a combination of both. Across all countries, a strong majority preferred human decision-making. A brief account of the expertise of the human decision maker increased that majority percentage only modestly (by 3% points). A brief account of the data on which the algorithm relies significantly reduced the size of the majority preferring the human decision-maker (by 11% points). When information about both the human and the algorithm was provided, the size of the majority preferring the human decision-maker was also significantly reduced (by 8% points). Other variables, above all prior experience with algorithms, were correlated with increases or decreases in the size of the majority favouring human decisionmaker or the algorithm. Prior experiences were significantly correlated with preferences; positive interactions were correlated with a reversal of the baseline preference for human decision-makers when algorithmic information was provided. Methodological robustness was ensured through OLS-, Logit-, and Poisson-regression, as well as Random Forest analyses. The findings suggest that informational interventions alone have a relatively modest effect on algorithm acceptance.