What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework

TL;DR

This study develops a predictive moral framework based on five core qualities that can accurately forecast public acceptability of diverse AI applications, aiding responsible AI development.

Contribution

It introduces a comprehensive, empirically validated framework that predicts public moral judgments of AI applications using five key moral qualities.

Findings

Five core moral qualities explain over 90% of acceptability variance.

The framework accurately predicts individual judgments for new applications.

The model demonstrates strong predictive power across different AI domains.

Abstract

As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and predictable. In a large, preregistered study (N = 587, U.S. representative sample), we used a comprehensive taxonomy of 100 AI applications spanning personal and organizational contexts-including both functional uses and the moral treatment of AI itself. In participants' collective judgment, applications ranged from highly unacceptable to fully acceptable. We found this variation was strongly predictable: five core moral qualities-perceived risk, benefit, dishonesty, unnaturalness, and reduced accountability-collectively explained over 90% of the variance in acceptability ratings. The framework demonstrated strong predictive power across all domains and successfully predicted individual-level judgments for held-out applications. These findings reveal that a structured moral psychology underlies public evaluation of new technologies, offering a powerful tool for anticipating public resistance and guiding responsible innovation in AI.