The study was published last month by researchers Zohar Gilad, Prof. Ofra Amir, and Prof. Liat Levontin from the Faculty of Industrial Engineering and Management at the Technion. It featured over 1,600 participants, and was published in the Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.
The researchers found that a system's "warmth," which they defined as the system's perceived intent and the primary beneficiary of the system, was more important to potential users than its competence.
According to the study, the participants' preferred a warm system that was built for the consumer, even when using an algorithm trained on less data, than a system using state-of-the-art artificial neural network algorithms that was built for the producer.
The study focused on AI systems such as navigation apps, search engines and recommender systems. This is in contrast to most research done on the topic so far, which used systems with a virtual or physical presence, such as virtual agents or robots. Based on the researchers' findings, they concluded that AI system designers communicate the AI's warmth to potential users.