Algorithm, Expert, or Both? Evaluating the Role of Feature Selection Methods on User Preferences and Reliance

TL;DR

This study examines how different feature selection methods in AI systems influence user preferences and reliance, revealing a gap between stated preferences and actual behavior, with implications for human-AI interaction design.

Contribution

It provides empirical evidence on user preferences and reliance patterns for algorithm-based, expert-based, and combined feature selection methods in AI systems.

Findings

Users prefer combined feature selection methods

Reliance on methods is equal regardless of assigned method

Preferences are domain-specific and do not influence reliance

Abstract

The integration of users and experts in machine learning is a widely studied topic in artificial intelligence literature. Similarly, human-computer interaction research extensively explores the factors that influence the acceptance of AI as a decision support system. In this experimental study, we investigate users' preferences regarding the integration of experts in the development of such systems and how this affects their reliance on these systems. Specifically, we focus on the process of feature selection -- an element that is gaining importance due to the growing demand for transparency in machine learning models. We differentiate between three feature selection methods: algorithm-based, expert-based, and a combined approach. In the first treatment, we analyze users' preferences for these methods. In the second treatment, we randomly assign users to one of the three methods and analyze whether the method affects advice reliance. Users prefer the combined method, followed by the expert-based and algorithm-based methods. However, the users in the second treatment rely equally on all methods. Thus, we find a remarkable difference between stated preferences and actual usage. Moreover, allowing the users to choose their preferred method had no effect, and the preferences and the extent of reliance were domain-specific. The findings underscore the importance of understanding cognitive processes in AI-supported decisions and the need for behavioral experiments in human-AI interactions.