The collection and use of demographic data in psychological sciences has the potential to aid in transforming inequities brought about by unjust social conditions toward equity. However, many current methods surrounding demographic data do not achieve this goal. Some methods function to reduce, but not eliminate, inequities, whereas others may perpetuate harmful stereotypes, invalidate minoritized identities, and exclude key groups from research participation or access to disseminated findings. In this article, we aim to (a) review key ethical and social-justice dilemmas inherent to working with demographic data in psychological research and (b) introduce a framework positioned in ethics and social justice to help psychologists and researchers in social-science fields make thoughtful decisions about the collection and use of demographic data. Although demographic data methods vary across subdisciplines and research topics, we assert that these core issues—and solutions—are relevant to all research within the psychological sciences, including basic and applied research. Our overarching aim is to support key stakeholders in psychology (e.g., researchers, funding agencies, journal editors, peer reviewers) in making ethical and socially-just decisions about the collection, analysis, reporting, interpretation, and dissemination of demographic data.
Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.
more » « less- PAR ID:
- 10362864
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Current Directions in Psychological Science
- Volume:
- 31
- Issue:
- 1
- ISSN:
- 0963-7214
- Format(s):
- Medium: X Size: p. 76-87
- Size(s):
- p. 76-87
- Sponsoring Org:
- National Science Foundation
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