Testing for Implicit Bias: Values, Psychometrics, and Science Communication

Abstract

Our understanding of implicit bias and how to measure it has yet to be settled. Various debates between cognitive scientists are unresolved. Moreover, the public’s under-standing of implicit bias tests continues to lag behind cognitive scientists’. These discrepancies pose potential problems. After all, a great deal of implicit bias research has been publicly funded. Further, implicit bias tests continue to feature in discourse about public- and private-sector policies surrounding discrimination, inequality, and even the purpose of science. We aim to do our part by reconstructing some of the re-cent arguments in ordinary language and then revealing some of the operative norms or values that are often hidden beneath the surface of these arguments. This may help the public learn more about the science of implicit bias. It may also help both laypeople and scientists reflect on the values, interests, and stakeholders involved in establishing, justifying, and communicating scientific research.

Human-Centered Design to Address Biases in Artificial Intelligence

“…Ethicists can evaluate the moral and ethical implications of the data bias and provide guidance on how to design AI systems in ways that align with societal values and ethical principles [ 46 ]. Communication scientists can develop effective strategies for communicating about the data bias, its capabilities, and limitations, and mitigate potential ethical and societal implications of data bias, such as issues related to privacy, bias, and fairness [ 47 ].…”