A Network Approach to Public Trust in Generative AI

As generative AI becomes more deeply integrated into society, building public trust in this technology has emerged as a key challenge for policymakers. Existing approaches, such as the European Commission’s Trustworthy AI framework, largely seek to tackle this issue by offering comprehensive technical and legal measures for promoting a more trustworthy AI industry. However, this paper argues that such approaches are limited in scope and do not fully account for the social complexity of generative AI. As these technologies can now replicate modes of human communication and contribute to our collective knowledge, they cannot be simply considered products to be regulated. Rather, they exist as active social actors and AI policy should reflect this. To better account for this social role, this paper develops a network approach to trust in AI inspired by philosophy of technology and Actor-Network Theory (ANT). This approach argues that trust emerges, first and foremost, from the material interactions between social actors involved in a vast and precarious network. In the context of generative AI, this material network extends far beyond the AI industry to include those various actors that are not directly involved in AI development but that nonetheless influence public trust. As such, this paper argues that the policy goal of establishing trustworthy AI, and thus promoting public trust in AI, is not solely a matter of promoting a more trustworthy AI industry. Rather, to achieve such a goal, more diverse policy solutions need to be devised on the basis of social interactions as part of a whole-of-society approach. Primarily, this paper highlights that public trust in generative AI is influenced by those actors that play a key role in socio-political discourse such as political figures, media organizations, academic institutions and government bodies, among others. As such, public trust in generative AI is linked to trust in our information environment more broadly. To conclude, the paper argues that policymakers seeking to promote trustworthy AI must first seek to combat the current post-truth political crisis and restore public trust in democratic institutions.