TY - JOUR
T1 - Auditing the AI Auditors
T2 - A Framework for Evaluating Fairness and Bias in High Stakes AI Predictive Models
AU - Landers, Richard N.
AU - Behrend, Tara S.
N1 - Publisher Copyright:
© 2022. American Psychological Association
PY - 2022
Y1 - 2022
N2 - Researchers, governments, ethics watchdogs, and the public are increasingly voicing concerns about unfairness and bias in artificial intelligence (AI)-based decision tools. Psychology's more-than-a-century of research on the measurement of psychological traits and the prediction of human behavior can benefit such conversations, yet psychological researchers often find themselves excluded due to mismatches in terminology, values, and goals across disciplines. In the present paper, we begin to build a shared interdisciplinary understanding of AI fairness and bias by first presenting three major lenses, which vary in focus and prototypicality by discipline, from which to consider relevant issues: (a) individual attitudes, (b) legality, ethicality, and morality, and (c) embedded meanings within technical domains. Using these lenses, we next present
psychological audits as a standardized approach for evaluating the fairness and bias of AI systems that make predictions about humans across disciplinary perspectives. We present 12 crucial components to audits across three categories: (a) components related to AI models in terms of their source data, design, development, features, processes, and outputs, (b) components related to how information about models and their applications are presented, discussed, and understood from the perspectives of those employing the algorithm, those affected by decisions made using its predictions, and third-party observers, and (c) meta-components that must be considered across all other auditing components, including cultural context, respect for persons, and the integrity of individual research designs used to support all model developer claims. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
AB - Researchers, governments, ethics watchdogs, and the public are increasingly voicing concerns about unfairness and bias in artificial intelligence (AI)-based decision tools. Psychology's more-than-a-century of research on the measurement of psychological traits and the prediction of human behavior can benefit such conversations, yet psychological researchers often find themselves excluded due to mismatches in terminology, values, and goals across disciplines. In the present paper, we begin to build a shared interdisciplinary understanding of AI fairness and bias by first presenting three major lenses, which vary in focus and prototypicality by discipline, from which to consider relevant issues: (a) individual attitudes, (b) legality, ethicality, and morality, and (c) embedded meanings within technical domains. Using these lenses, we next present
psychological audits as a standardized approach for evaluating the fairness and bias of AI systems that make predictions about humans across disciplinary perspectives. We present 12 crucial components to audits across three categories: (a) components related to AI models in terms of their source data, design, development, features, processes, and outputs, (b) components related to how information about models and their applications are presented, discussed, and understood from the perspectives of those employing the algorithm, those affected by decisions made using its predictions, and third-party observers, and (c) meta-components that must be considered across all other auditing components, including cultural context, respect for persons, and the integrity of individual research designs used to support all model developer claims. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
KW - Artificial intelligence
KW - Audit
KW - Bias
KW - Machine learning
KW - Psychology
UR - http://www.scopus.com/inward/record.url?scp=85125509681&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125509681&partnerID=8YFLogxK
U2 - 10.1037/amp0000972
DO - 10.1037/amp0000972
M3 - Article
C2 - 35157476
AN - SCOPUS:85125509681
JO - American Psychologist
JF - American Psychologist
SN - 0003-066X
ER -