Zhang Qing, Elsweiler David, Trattner Christoph
Institute for Language, Literature and Culture, University of Regensburg, Universitätsstrße 31, 93053 Regensburg, Germany.
Department of Information Science & Media Studies, University of Bergen, Fosswinckelsgt. 6, 5007 Bergen, Norway.
Foods. 2020 Jun 23;9(6):823. doi: 10.3390/foods9060823.
This article investigates how visual biases influence the choices made by people and machines in the context of online food. To this end the paper investigates three research questions and shows (i) to what extent machines are able to classify images, (ii) how this compares to human performance on the same task and (iii) which factors are involved in the decision making of both humans and machines. The research reveals that algorithms significantly outperform human labellers on this task with a range of biases being present in the decision-making process. The results are important as they have a range of implications for research, such as recommender technology and crowdsourcing, as is discussed in the article.
本文研究视觉偏差如何在在线食品的背景下影响人和机器做出的选择。为此，本文研究了三个研究问题，并表明：（i）机器在多大程度上能够对图像进行分类；（ii）这与人类在同一任务上的表现相比如何；以及（iii）人和机器的决策过程中涉及哪些因素。研究表明，在这项任务中，算法明显优于人工标注者，且决策过程中存在一系列偏差。正如文章中所讨论的，这些结果很重要，因为它们对推荐技术和众包等研究有一系列影响。
