How Tech Workers Contend with Hazards of Humanlikeness in Generative AI

TL;DR

This study explores how technology workers perceive and manage the risks associated with the humanlike qualities of generative AI, highlighting the need for clearer understanding and support to mitigate potential harms.

Contribution

It provides empirical insights into workers' perspectives on AI humanlikeness and introduces a conceptual map linking hazards to misconceptions of humanlike qualities.

Findings

Workers have diverse views on AI humanlikeness and associated risks.

There is an unsettled knowledge environment regarding AI hazards.

A conceptual map links hazards to misconceptions of humanlikeness.

Abstract

Generative AI's humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholars about potential hazards and harms, such as overtrust in system outputs. To investigate how technology workers navigate these humanlike qualities and anticipate emergent harms, we conducted focus groups with 30 professionals across six job functions (ML engineering, product policy, UX research and design, product management, technology writing, and communications). Our findings reveal an unsettled knowledge environment surrounding humanlike generative AI, where workers' varying perspectives illuminate a range of potential risks for individuals, knowledge work fields, and society. We argue that workers require comprehensive support, including clearer conceptions of ``humanlikeness'' to effectively mitigate these risks. To aid in mitigation strategies, we provide a conceptual map articulating the identified hazards and their connection to conflated notions of ``humanlikeness.''