This commentary draws critical attention to the ongoing commodification of trust in policy and scholarly discourses of artificial intelligence (AI) and society. Based on an assessment of publications discussing the implementation of AI in governmental and private services, our findings indicate that this discursive trend towards commodification is driven by the need for a trusting population of service users to harvest data at scale and leads to the discursive construction of trust as an essential good on a par with data (...) as raw material. This discursive commodification is marked by a decreasing emphasis on trust understood as the expected reliability of a trusted agent, and increased emphasis on instrumental and extractive framings of trust as a resource. This tendency, we argue, does an ultimate disservice to developers, users, and systems alike, insofar as it obscures the subtle mechanisms through which trust in AI systems might be built, making it less likely that it will be. (shrink)

As public administrations adopt artificial intelligence (AI), we see this transition has the potential to transform public service and public policies, by offering a rapid turnaround on decision making and service delivery. However, a recent series of criticisms have pointed to problematic aspects of mainstreaming AI systems in public administration, noting troubled outcomes in terms of justice and values. The argument supplied here is that any public administration adopting AI systems must consider and address ambiguities and uncertainties surrounding two key (...) dimensions: the algorithms’ results, and how public managers make decisions for and about AI systems’ design. The article is based on bibliographic research in institutional theory perspective that relates the design of AI systems and relevant literature on the decision-making process in public policy. The main finding is to explain why autonomous decision systems continue to reproduce ambiguities and uncertainties when applied in public administration and propose a reflection on AI governance in public administration. This article points to the need for design institutions that immerse themselves in understanding the nuances, details, and potential outcomes of AI governance for public administration. Such institutions would reconcile consequentialist logic with a logic of appropriateness to help navigate and mediate ambiguities and uncertainties. (shrink)