Abstract: This study explores preferences for algorithmic versus 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 three percentage points). A brief account of the data on which the algorithm relies significantly reduced the size of the majority preferring the human decisionmaker (by eleven percentage points). When information about both the human and the algorithm was provided, the size of the majority preferring the human decisionmaker was also significantly reduced (by eight percentage points). Other variables, above all prior experience with algorithms, were correlated with increases or decreases in the size of the majority favouring human decision-maker or the algorithm. Prior experiences were significantly correlated with preferences, with positive interactions reversing the baseline preference for human decisionmakers 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.