 In the vast ecosystem of big data infrastructures, it is difficult to keep track of such distortions and assess their significance for data interpretation, especially in situations where heterogeneous data sources structured through appeal to different values are mashed together. Thus, the systematic aggregation of convenient datasets and analytic tools over others often results in a big data pool where the relevant sources and forms of bias are impossible to locate and account for (Pasquale 2015; O’Neill 2016; Zuboff 2017; Leonelli 2019a). In such a landscape, arguments for a separation between fact and value—and even a clear distinction between the role of epistemic and non-epistemic values in knowledge production—become very difficult to maintain without discrediting the whole edifice of big data science. Given the extent to which this approach has penetrated research in all domains, it is arguably impossible, however, to critique the value-laden structure of big data science without calling into question the legitimacy of science itself. A more constructive approach is to embrace the extent to which big data science is anchored in human choices, interests and values, and ascertain how this affects philosophical views on knowledge, truth and method.