IndoBias: A Dual Track Culturally Grounded Benchmark for LLMs Bias Evaluation in Indonesian Languages
arXiv:2606.01260v1 Announce Type: new Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia, thus leaving a critical gap in evaluating representational fairness and localized stereotypes within its uniquely vast, multilingual, and diverse sociocultural landscape. To address this, we introduce IndoBias as a culturally-grounded bias benchmark to assess LLMs bias in Indonesian and three local languages: Javanese, Sundanese, and Makasar. IndoBias features dual perspective evaluation tracks: d
The rapid deployment and increasing capabilities of Large Language Models necessitate detailed, culturally specific bias evaluations as their global footprint expands.
This benchmark highlights the critical need for localized and culturally grounded bias assessments to ensure representational fairness and prevent the perpetuation of stereotypes in AI systems operating in diverse regions.
The availability of a specific benchmark for Indonesian languages will enable more rigorous and nuanced bias evaluations for LLMs beyond English-centric datasets, impacting their deployment and refinement in multilingual societies.
- · Indonesian AI developers
- · Multilingual LLM researchers
- · Ethical AI advocates
- · Local language preservation efforts
- · LLM providers with unaddressed bias
- · Generative AI tools without localized testing
- · Homogenized global AI deployments
Improved fairness and cultural relevance of LLMs in Indonesian and related languages.
Increased demand for culturally specific datasets and benchmarks across other diverse linguistic regions.
The emergence of 'Glocal AI' solutions, prioritizing local context and cultural nuances alongside global capabilities.
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Read at arXiv cs.CL