Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory
This work addresses the need for public engagement in AI governance to capture diverse perspectives, though it is incremental as it applies an existing theory to new data without proposing novel methods.
The study tackled the problem of understanding public opinion on responsible AI by applying Cultural Consensus Theory to a nationally representative survey dataset in the U.S., resulting in insights into shared and contrasting views that can inform developers and policymakers.
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.