Abstract
Scientists have long used physiological recording to study bodily processes underlying emotions. However, sensors are often affected by hair or skin characteristics that impair the accuracy of their measurements in Black individuals. We draw attention to an underrecognized consequence of such recording biases: differential exclusion of Black participants during Quality Control (QC) stages of the data pipeline, what we call the QC exclusion gap. These routine procedures receive less attention than other stages of the research process; yet, disparate participant exclusions during QC can undermine the internal and external validity of research utilizing physiological recording. Using Electrodermal Activity (EDA) as an illustrative example, we walk through QC analyses of Black (N= 72) and White (N=68) participants’ Skin Conductance Levels (SCL) during a baseline passive listening task. Black participants exhibited significantly lower SCL (M = 4.13μS, SD = 2.49) than White participants (M = 7.95μS, SD = 3.19), p < .001. As a result, Black participants’ EDA data received lower quality grades during QC, leading to differential attrition during triage. We offer immediate recommendations to compensate for the QC exclusion gap, such as integrating quality monitoring feedback into recruitment procedures and reporting exclusion rates by demographic group, and identify broader directions for sensor development and analytical methods. Quality control is often considered a prelude to data analysis; by attending to who is excluded or included, however, we can design a more inclusive science.