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Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory

Necdet Gurkan, Jordan W. Suchow

TL;DR

This study addresses how public attitudes toward responsible AI vary across the US by applying an extended Cultural Consensus Theory (iCCT) to a nationally representative survey (N=2105, Morning Consult, Sept 2021) spanning 62 items on six AI-related topics. It shows that public opinion is not monolithic but partitions into four cultural consensus groups, including one large cluster, with notable inter- and intra-cultural variation and a common concern about AI transparency across most cultures. Methodologically, the authors advance the field by introducing a Bayesian nonparametric iCCT that infers the number of cultural groups via a Dirichlet Process, enabling data-driven discovery of cultural structure without fixing dimensionality. The findings have practical implications for developers and policymakers, highlighting that governance and communication about responsible AI should be tailored to distinct cultural perspectives to enhance engagement, trust, and policy effectiveness.

Abstract

As the societal implications of Artificial Intelligence (AI) continue to grow, the pursuit of responsible AI necessitates public engagement in its development and governance processes. This involvement is crucial for capturing diverse perspectives and promoting equitable practices and outcomes. We applied Cultural Consensus Theory (CCT) to a nationally representative survey dataset on various aspects of AI to discern beliefs and attitudes about responsible AI in the United States. Our results offer valuable insights by identifying shared and contrasting views on responsible AI. Furthermore, these findings serve as critical reference points for developers and policymakers, enabling them to more effectively consider individual variances and group-level cultural perspectives when making significant decisions and addressing the public's concerns.

Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory

TL;DR

This study addresses how public attitudes toward responsible AI vary across the US by applying an extended Cultural Consensus Theory (iCCT) to a nationally representative survey (N=2105, Morning Consult, Sept 2021) spanning 62 items on six AI-related topics. It shows that public opinion is not monolithic but partitions into four cultural consensus groups, including one large cluster, with notable inter- and intra-cultural variation and a common concern about AI transparency across most cultures. Methodologically, the authors advance the field by introducing a Bayesian nonparametric iCCT that infers the number of cultural groups via a Dirichlet Process, enabling data-driven discovery of cultural structure without fixing dimensionality. The findings have practical implications for developers and policymakers, highlighting that governance and communication about responsible AI should be tailored to distinct cultural perspectives to enhance engagement, trust, and policy effectiveness.

Abstract

As the societal implications of Artificial Intelligence (AI) continue to grow, the pursuit of responsible AI necessitates public engagement in its development and governance processes. This involvement is crucial for capturing diverse perspectives and promoting equitable practices and outcomes. We applied Cultural Consensus Theory (CCT) to a nationally representative survey dataset on various aspects of AI to discern beliefs and attitudes about responsible AI in the United States. Our results offer valuable insights by identifying shared and contrasting views on responsible AI. Furthermore, these findings serve as critical reference points for developers and policymakers, enabling them to more effectively consider individual variances and group-level cultural perspectives when making significant decisions and addressing the public's concerns.
Paper Structure (11 sections, 5 figures, 1 table)

This paper contains 11 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cultural Consensus Theory
  4. Infinite Cultural Consensus Theory
  5. Methods
  6. Data collection
  7. Questionnaires
  8. Results
  9. Consensus beliefs regarding responsible AI
  10. Controversial and challenging topics in responsible AI: Inter and intra-cultural consensus beliefs
  11. Discussion

Figures (5)

  • Figure 1: Cultural cluster's posterior mean results of consensus values from the responsible AI survey items. Circles (culture 1), stars (culture 2), squares (culture 3), and triangles (culture 4) respectively represent the 'truths' of the four identified cultures.
  • Figure 2: Cultural clusters' posterior mean results for each main question. Circles, stars, squares, and triangles respectively represent the 'truths' of the four identified cultures.
  • Figure 3: Posterior mean results of the item difficulties for each culture.
  • Figure 4: Demographic Distribution of Cultural Assignments
  • Figure 5: Geographic Distribution of Cultural Assignments