Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation
arXiv:2606.02528v1 Announce Type: cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply it to Bitcoin. First, a behavioral audit of eight frontier LLMs shows that Bitcoin's ranking among money-lik
The proliferation of Large Language Models (LLMs) in financial applications necessitates immediate scrutiny into their inherent biases as they transition from research to deployment.
Biases in financial LLMs can lead to systematically skewed financial advice and portfolio allocations, posing significant risks for investors and potentially destabilizing markets.
The focus is shifting from simply deploying LLMs in finance to actively auditing and understanding their internal representations and preferences for specific assets, impacting regulatory and development protocols.
- · AI ethicists and auditors
- · Regulatory bodies
- · Retail investors (via improved transparency)
- · Unregulated 'robo-advisors'
- · LLM developers ignoring bias audits
- · Investors exposed to biased agents
Increased demand for robust auditing frameworks and tools for financial AI models.
Regulatory bodies may mandate bias audits for all AI-powered financial advisory tools, shaping product development.
The transparency unearthed could influence asset valuations, particularly for assets found to be systematically favored or disfavored by prominent AI models.
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Read at arXiv cs.LG