Decoding Demographic un-fairness from Indian Names

• URL: http://arxiv.org/abs/2209.03089v1
• Date: Wed, 7 Sep 2022 11:54:49 GMT
• Title: Decoding Demographic un-fairness from Indian Names
• Authors: Medidoddi Vahini, Jalend Bantupalli, Souvic Chakraborty, and Animesh Mukherjee
• Abstract summary: Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems. We collect three publicly available datasets to train state-of-the-art classifiers in the domain of gender and caste classification. We perform cross-testing (training and testing on different datasets) to understand the efficacy of the above models.
• Score: 4.402336973466853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems. Important fields like education and politics, which often lay a foundation for the future of equality in society, need scrutiny to design policies that can better foster equality in resource distribution constrained by the unbalanced demographic distribution of people in the country. We collect three publicly available datasets to train state-of-the-art classifiers in the domain of gender and caste classification. We train the models in the Indian context, where the same name can have different styling conventions (Jolly Abraham/Kumar Abhishikta in one state may be written as Abraham Jolly/Abishikta Kumar in the other). Finally, we also perform cross-testing (training and testing on different datasets) to understand the efficacy of the above models. We also perform an error analysis of the prediction models. Finally, we attempt to assess the bias in the existing Indian system as case studies and find some intriguing patterns manifesting in the complex demographic layout of the sub-continent across the dimensions of gender and caste.

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