The World Wide Recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation

• URL: http://arxiv.org/abs/2406.09496v3
• Date: Sun, 09 Feb 2025 17:13:58 GMT
• Title: The World Wide Recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
• Authors: Jabez Magomere, Shu Ishida, Tejumade Afonja, Aya Salama, Daniel Kochin, Foutse Yuehgoh, Imane Hamzaoui, Raesetje Sefala, Aisha Alaagib, Samantha Dalal, Beatrice Marchegiani, Elizaveta Semenova, Lauren Crais, Siobhan Mackenzie Hall,
• Abstract summary: We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection.<n>We analyse bias operationalisation to highlight how current systems underperform across several dimensions.<n>We find that these T2I models generally do not produce quality outputs of dishes specific to various regions.
• Score: 3.505416621482746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes evaluation dataset. We also analyse bias operationalisation to highlight how current systems underperform across several dimensions: (in-)accuracy, (mis-)representation, and cultural (in-)sensitivity, with evidence from qualitative community-based observations and quantitative automated tools. We find that these T2I models generally do not produce quality outputs of dishes specific to various regions. This is true even for the US, which is typically considered more well-resourced in training data -- although the generation of US dishes does outperform that of the investigated African countries. The models demonstrate the propensity to produce inaccurate and culturally misrepresentative, flattening, and insensitive outputs. These representational biases have the potential to further reinforce stereotypes and disproportionately contribute to erasure based on region. The dataset and code are available at https://github.com/oxai/world-wide-dishes.

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