Measuring public opinion towards artificial intelligence: development and validation of a general AI attitude short scale

Novotny, Marcus; Weber, Wiebke; Kern, Christoph und Kreuter, Frauke (2025): Measuring public opinion towards artificial intelligence: development and validation of a general AI attitude short scale. In: AI & SOCIETY [Forthcoming]

Abstract

The rapid proliferation of artificial intelligence (AI) has sparked both enthusiasm and ethical concerns in societies. As AI continues to permeate daily life, policymakers need to understand how it is perceived by diverse stakeholders and communities. To reliably measure attitudes towards AI of the general public, a short scale is essential for universal application. Existing scales face limitations in applicability due to their length, sub-standard internal consistency, or a focus on only negative attitudes. In response, we built up on existing scales and developed a unidimensional six-item general AI attitude short scale. First tests on internet panel data from Germany (n = 1001) and the US (n = 3091) obtained favorable results for classical test theory (CTT) and item response theory (IRT). Confirmatory factor analysis indicated an excellent fit for a single-factor structure, while the scale also exhibited strong criterion-related validity, correlating positively with digital competency and predicting acceptance of several AI applications. Additional IRT analyses suggested high item discrimination, broad coverage of the attitude spectrum and no meaningful differential item functioning (DIF). Thus, we propose a psychometrically sound short scale for measuring general AI attitude and provide insights into the antecedents and consequences of the construct.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISSN: 0951-5666
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 460037581