AmchiBias: Measuring Stereotypical Bias in Goan Identity Groups with a Minimal Pair Dataset in English and Konkani
arXiv:2606.15191v1 Announce Type: new Abstract: Socio-cultural stereotypical bias is an important consideration in the development and deployment of NLP systems. It is however often considered only at the national level, despite rich subnational socio-cultural structures. We present AmchiBias, the first benchmark for measuring socio-cultural stereotypical bias for the Indian state of Goa with its unique historically multicultural setting. It covers various Goan identity groups and comprises 313 minimal pairs across eight sociodemographic dimensions in both English and Devanagari Konkani. We th
The increasing focus on responsible AI development highlights the need for nuanced bias detection beyond national-level considerations.
This benchmark provides critical tools for developing more equitable and culturally aware NLP systems, especially in diverse linguistic and social contexts.
The ability to measure and address socio-cultural biases at a subnational level in AI systems, moving beyond generalized global or national datasets.
- · AI ethicists
- · NLP developers
- · Multilingual AI platforms
- · Indian language tech sector
- · Unaware AI developers
- · Generic bias detection tools
Improved fairness and relevance of NLP applications for diverse cultural groups within India and similar regions.
Increased research and investment into culturally specific AI training data and bias mitigation techniques for non-Western languages.
The emergence of 'local AI' standards and regulations that recognize and mandate subnational cultural sensitivity in AI deployments.
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