Cheap Expertise: Mapping and Challenging Industry Perspectives in the Expert Data Gig Economy

TL;DR

This paper examines how industry leaders portray AI and human expertise in the expert gig economy, highlighting the perception of AI expertise as cheap and the need for reforming institutional expertise.

Contribution

It provides an analysis of industry narratives on expertise, revealing perceptions of AI as cost-effective and the call for reforming traditional expertise institutions.

Findings

Industry views AI expertise as offering better ROI than human expertise.

Human expertise is seen as an extractable resource with value judged relative to AI.

Institutional expertise is considered in need of reform to integrate into AI systems.

Abstract

Demand for expert-annotated data on the part of leading AI labs has created an expert gig economy with the potential to reshape white collar work and society's understanding of expertise. In this research, we study the vision for the future of expertise described in the public communication of five industry data annotation organizations and their CEOs, as reflected on social media feeds and public appearances on podcasts. We find that the industry envisions AI expertise as cheap, meaning that it can offer a better return on investment than human expertise. Human expertise, meanwhile, is viewed as an extractable resource, the value of which can be judged relative to AI expertise. Finally, institutional expertise (such as that created or possessed by universities and corporations) is viewed as in need of liberation or reform, such that it can be incorporated into the latest artificial intelligence systems. Our findings have implications for human experts, whose professional lives may be transformed and revalued by this industry, as well as for societal institutions that mediate expertise. We close this work with a series of provocations intended to elicit consideration of how society can best approach an AI-driven expert gig economy and the cheap expertise it intends to produce.