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Testing for implicit bias: Values, psychometrics, and science communication.

Nick Byrd1, Morgan Thompson2

  • 1College of Arts and Letters, Stevens Institute of Technology, Hoboken, NJ, USA.

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Summary
This summary is machine-generated.

Implicit bias research and its measurement are debated among scientists, with public understanding lagging. This work clarifies arguments and underlying values to improve comprehension of implicit bias science.

Keywords:
affect misattribution procedureevaluative priming taskimplicit association testimplicit biasindirect measurementphilosophy of cognitive sciencephilosophy of sciencepsychometricssocial psychologyvalues in science

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Area of Science:

  • Cognitive Biology
  • Social Development

Background:

  • The scientific understanding and measurement of implicit bias remain unsettled, with ongoing debates among cognitive scientists.
  • Public comprehension of implicit bias tests lags behind scientific discourse, despite the tests' use in policy discussions.

Purpose of the Study:

  • To reconstruct recent arguments on implicit bias in accessible language.
  • To reveal the underlying norms and values influencing these scientific debates.
  • To enhance public and scientific understanding of implicit bias research and its societal implications.

Main Methods:

  • Analysis and reconstruction of existing arguments regarding implicit bias.
  • Identification and explication of operative norms and values within scientific discourse.

Main Results:

  • Arguments surrounding implicit bias and its measurement are complex and often lack clear articulation.
  • Underlying values and stakeholder interests significantly influence the framing and communication of implicit bias research.

Conclusions:

  • Clarifying scientific arguments and their underlying values can improve public understanding of implicit bias.
  • Promoting reflection on values, interests, and stakeholders is crucial for responsible scientific communication and policy development.