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Open AccessArticle

Visual Cultural Biases in Food Classification

1
Institute for Language, Literature and Culture, University of Regensburg, Universitätsstrße 31, 93053 Regensburg, Germany
2
Department of Information Science & Media Studies, University of Bergen, Fosswinckelsgt. 6, 5007 Bergen, Norway
*
Author to whom correspondence should be addressed.
Foods 2020, 9(6), 823; https://doi.org/10.3390/foods9060823
Received: 7 June 2020 / Revised: 12 June 2020 / Accepted: 15 June 2020 / Published: 23 June 2020
(This article belongs to the Special Issue How Visuals Affect Food Choice?)
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. View Full-Text
Keywords: visual biases; food classification; crowdsourcing visual biases; food classification; crowdsourcing
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MDPI and ACS Style

Zhang, Q.; Elsweiler, D.; Trattner, C. Visual Cultural Biases in Food Classification. Foods 2020, 9, 823.

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