The Human in the Infinite Loop: A Case Study on Revealing and Explaining Human-AI Interaction Loop Failures

TL;DR

This paper investigates failures in human-AI interaction loops through a case study and lab experiments, revealing issues with preference-based optimization and user biases, and proposes UI guidelines to improve system convergence.

Contribution

It identifies key challenges in human-in-the-loop AI systems, especially regarding inconsistent judgments and bias influence, and offers practical UI design recommendations.

Findings

Human-AI loops often fail to converge to satisfactory results.

Preference-based optimization struggles with inconsistent human judgments.

User biases influence future interactions and system outcomes.

Abstract

Interactive AI systems increasingly employ a human-in-the-loop strategy. This creates new challenges for the HCI community when designing such systems. We reveal and investigate some of these challenges in a case study with an industry partner, and developed a prototype human-in-the-loop system for preference-guided 3D model processing. Two 3D artists used it in their daily work for 3 months. We found that the human-AI loop often did not converge towards a satisfactory result and designed a lab study (N=20) to investigate this further. We analyze interaction data and user feedback through the lens of theories of human judgment to explain the observed human-in-the-loop failures with two key insights: 1) optimization using preferential choices lacks mechanisms to deal with inconsistent and contradictory human judgments; 2) machine outcomes, in turn, influence future user inputs via heuristic biases and loss aversion. To mitigate these problems, we propose descriptive UI design guidelines. Our case study draws attention to challenging and practically relevant imperfections in human-AI loops that need to be considered when designing human-in-the-loop systems.