Year: 2026
Type: conference-paper
Abstract: Text-embedding models frequently inherit societal biases, yet the influence of socio-economic markers remains largely unexplored.This paper identifies Currency Bias as a systemic representational limitation in financial AI, where models exhibit associative sensitivity to economic hierarchies.We analyze this through three dimensions: (1) the Syntax Gap, where models fail to align currency names, symbols, and acronyms; (2) Associative Sensitivity, where embeddings disproportionately link specific currency identifiers to narratives of risk or poverty; and (3) Downstream volatility, where currency substitutions induce predictive entropy, sentence misunderstanding, sentiment shifts and credit default prediction flips in downstream tasks.Benchmarking 14 stateof-the-art architectures reveals a pervasive phenomenon of representational disparity, affecting several currencies.These findings suggest that current embedding practices inadvertently encode inequalities, posing significant risks for the fairness and reliability of global financial NLP applications 1 .* This work was carried out while the author was at Mastercard AI Garage.
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166884717