Are you aware of what you are watching? Role of machine heuristic in online content recommendations

TL;DR

This study investigates how users' trust in machine recommendations influences their attitudes, especially regarding inappropriate content, highlighting the role of machine heuristic and algorithm aversion in online content consumption.

Contribution

It introduces an experimental approach to examine the impact of machine heuristic on user attitudes toward content recommendations, including inappropriate material.

Findings

Participants favored machine recommendations over human ones.

High trust in machines correlated with more positive attitudes.

Algorithm aversion affected perceptions of machine-recommended content.

Abstract

Since recommender systems have been created and developed to automate the recommendation process, users can easily consume their desired video content on online platforms. In this line, several content recommendation algorithms are introduced and employed to allow users to encounter content of their interests, effectively. However, the recommendation systems sometimes regrettably recommend inappropriate content, including misinformation or fake news. To make it worse, people would unreservedly accept such content due to their cognitive heuristic, machine heuristic, which is the rule of thumb that machines are more accurate and trustworthy than humans. In this study, we designed and conducted a web-based experiment where the participants are invoked machine heuristic by experiencing the whole process of machine or human recommendation system. The results demonstrated that participants (N = 89) showed a more positive attitude toward a machine recommender than a human recommender, even the recommended videos contain inappropriate content. While participants who have a high level of trust in machines exhibited a negative attitude toward recommendations. Based on these results, we suggest that a phenomenon known as algorithm aversion might be simultaneously considered with machine heuristic in investigating human interaction with a machine.