Navigating fairness: introducing the multidimensional AIM-FAIR scale for evaluating AI decision-making
Year: 2025
Type: article
Abstract: People’s concerns regarding the fairness of algorithmic decision-making, coupled with its expanding utilization across various spheres of our lives underscores the need for robust measures to assess perceived fairness in standardized survey research. Existing fairness scales often suffer from inadequate content coverage, particularly in terms of Perceived Group Discrimination , and frequently employ suboptimal measurement methods, such as single-item assessments. This paper introduces the AIM-FAIR scale, a multidimensional tool grounded in classical test theory, employing Likert-scaled answering options and a reflective measurement model. Developed through four studies ( n = 1777) and validated in both English and German, the scale includes 17 items across five subscales: Perceived Consistency , Perceived Equity , Perceived Group Bias , Perceived Manipulability , and Perceived (Explanatory) Transparency . Both language versions demonstrate excellent fit indices and consistent measurement invariance across diverse backgrounds, languages, and conditions. The AIM-FAIR scale offers higher ecological validity and a more comprehensive framework for evaluating fairness in ADM, enhancing cross-cultural and cross-linguistic research on AI fairness.
Source: AI & Society
Authors: Nico Ehrhardt, Manuela Renn, Sonja Utz
Institutions: Leibniz-Institut für Wissensmedien, University of Tübingen
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W4409775478