Current online review systems widely suffer from rating biases. Biased ratings can lead to violations of customer trust and failures of business intelligence. Hence, both practitioners and researchers have directed massive efforts toward curbing rating biases. In this paper, we investigate bandwagon bias, the rating distortion resulting from individuals posting ratings shifted toward the displayed average rating, and propose a bias warning approach to mitigate this bias. Drawing on the flexible correction model, the theory of valuation in behavioral economics, and previous warning research, we design an eligible warning strategy in two steps. First, we start with the “risk-alert” warning strategy that prior research widely employs and rationalize its deficiencies by synthesizing theoretical analysis and extant empirical evidence. Second, considering the deficiencies, we identify a supplementary content design factor, ranking tasks, and construct a “risk-alert-with-ranking-task” warning strategy. We then empirically test the effects of the two warning strategies on individual ratings in both cases where bandwagon bias occurs and does not occur in their initial assessments. The results of four controlled experiments indicate that (1) the risk-alert strategy can reduce bandwagon bias in individual ratings, but will elicit unwanted rating distortions when bandwagon bias does not occur in individuals’ initial assessments; (2) the risk-alert-with-ranking-task strategy can mitigate bandwagon bias and avoid the above unwanted rating distortions, thus can work as an eligible warning strategy. Our research contributes to the literature by proposing an eligible de-biasing solution for bandwagon bias and a bias warning approach for online rating de-biasing, which can help increase rating informativeness in online platforms.