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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Visual Cultural Biases in Food Classification.

Qing Zhang1, David Elsweiler1, Christoph Trattner2

  • 1Institute for Language, Literature and Culture, University of Regensburg, Universitätsstrße 31, 93053 Regensburg, Germany.

Foods (Basel, Switzerland)
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Machine and human visual biases in online food image classification were studied. Algorithms outperformed humans, revealing biases in both decision-making processes with implications for recommender systems.

Keywords:
crowdsourcingfood classificationvisual biases

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Visual biases impact decision-making in both humans and artificial intelligence.
  • Understanding these biases is crucial for developing fair and accurate AI systems, especially in data-driven fields like online food platforms.

Purpose of the Study:

  • To investigate and compare visual biases in human and machine image classification for online food.
  • To determine the extent of machine image classification capabilities.
  • To identify factors influencing decision-making in both humans and machines.

Main Methods:

  • Comparative analysis of human and machine performance on food image classification tasks.
  • Investigation of decision-making factors through analysis of biases.
  • Evaluation of algorithmic performance against human benchmarks.

Main Results:

  • Algorithms demonstrate superior performance in classifying food images compared to human labelers.
  • Significant visual biases were identified in the decision-making processes of both humans and algorithms.
  • Specific factors contributing to these biases were elucidated.

Conclusions:

  • AI algorithms show higher accuracy in food image classification than humans, but are not bias-free.
  • The findings have significant implications for the design of recommender systems and crowdsourcing platforms.
  • Further research is needed to mitigate identified biases in AI and human decision-making.