Auditing Algorithmic Bias in Transformer-Based Trading

Armin Gerami; Ramani Duraiswami

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arXiv · 2510.05140

Auditing Algorithmic Bias in Transformer-Based Trading

Armin Gerami·Ramani Duraiswami

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Abstract

Transformer models have become increasingly popular in financial applications, yet their potential risk making and biases remain under-explored. The purpose of this work is to audit the reliance of the model on volatile data for decision-making, and quantify how the frequency of price movements affects the model's prediction confidence. We employ a transformer model for prediction, and introduce a metric based on Partial Information Decomposition (PID) to measure the influence of each asset on the model's decision making. Our analysis reveals two key observations: first, the model disregards data volatility entirely, and second, it is biased toward data with lower-frequency price movements.

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Armin Gerami, Ramani Duraiswami. 2025-10-01. Auditing Algorithmic Bias in Transformer-Based Trading. https://arxiv.org/abs/2510.05140

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