Designing Human-AI Systems: Anthropomorphism and Framing Bias on Human-AI Collaboration

TL;DR

This study examines how anthropomorphism and framing biases influence human-AI collaboration in hiring, revealing that anthropomorphic AI affects user agreement, while framing does not, emphasizing tailored AI design.

Contribution

It provides empirical evidence on the effects of anthropomorphism and framing biases on human-AI decision-making in a hiring context, highlighting the importance of design considerations.

Findings

Anthropomorphism reduces agreement with AI recommendations.

Framing bias has no significant effect on decision-making.

Cognitive biases influence human-AI collaboration outcomes.

Abstract

AI is redefining how humans interact with technology, leading to a synergetic collaboration between the two. Nevertheless, the effects of human cognition on this collaboration remain unclear. This study investigates the implications of two cognitive biases, anthropomorphism and framing effect, on human-AI collaboration within a hiring setting. Subjects were asked to select job candidates with the help of an AI-powered recommendation tool. The tool was manipulated in a 3 x 3 between-subjects design to present three different AI identities (human-like, robot-like, generic) and three types of framing (positive, negative, and neutral). The results revealed that the framing of AI's recommendations had no significant influence on subjects' decisions. In contrast, anthropomorphism significantly affected subjects' agreement with AI recommendations. Subjects were less likely to agree with the AI if it had human-like characteristics. These findings demonstrate that cognitive biases can impact human-AI collaboration and highlight the need for tailored approaches to AI product design, rather than a single, universal solution.