Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control

TL;DR

This study investigates whether providing users with control over the process of model training, in addition to outcome control, can reduce algorithm aversion, revealing that process control over algorithm choice can mitigate aversion but input control does not.

Contribution

It is the first to compare process control with outcome control in mitigating algorithm aversion and highlights the limited additional benefit of combining both controls.

Findings

Process control over algorithm choice reduces algorithm aversion.

Changing input factors does not mitigate algorithm aversion.

Combining outcome and process control does not outperform outcome control alone.

Abstract

Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over how models' predictions are incorporated into decision-making mitigates algorithm aversion. We study whether algorithm aversion is mitigated by process control, wherein users can decide what input factors and algorithms to use in model training. We conduct a replication study of outcome control, and test novel process control study conditions on Amazon Mechanical Turk (MTurk) and Prolific. Our results partly confirm prior findings on the mitigating effects of outcome control, while also forefronting reproducibility challenges. We find that process control in the form of choosing the training algorithm mitigates algorithm aversion, but changing inputs does not. Furthermore, giving users both outcome and process control does not reduce algorithm aversion more than outcome or process control alone. This study contributes to design considerations around mitigating algorithm aversion.