![]() ![]() We have observed that researchers can significantly misinterpret the effects of biases, both overstating and understating the significance of certain biases. As curators and analysts of multiple large, popular data projects, we are uniquely aware of biases that are present when collecting and using event data. In conflict data, skepticism about potentially damaging biases can cause doubt about whether data collection procedures create systematic omissions, inflations, or misrepresentations due to the aforementioned prejudices or directionality. Bias occurs when a dataset deviates from a pure/comprehensive model of reality in non-random ways that may produce misleading and harmful inferences if not accounted for properly. All data have biases, which we define as a systematic inclination, prejudice, or directionality to information. With increased availability of disaggregated conflict event data for analysis, there are new and old concerns about bias. We therefore posit an agenda for data responsibility considering its collection and critical interpretation. We contend that it is necessary to advance an open and honest discussion about the responsibilities of all stakeholders in the data ecosystem – collectors, researchers, and those interpreting and applying findings – to thoughtfully and transparently reflect on those biases use data in good faith and acknowledge limitations. As curators and analysts of large, popular data projects, we are uniquely aware of biases that are present when collecting and using event data. ![]() In conflict data, there are often perceptions of damaging bias, and skepticism can emanate from several areas, including confidence in whether data collection procedures create systematic omissions, inflations, or misrepresentations. ![]() All data have biases, which we define as an inclination, prejudice, or directionality to information. ![]()
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