arXiv:2606.02214v1 Announce Type: new Abstract: Large language models are increasingly used in value-sensitive decision settings, where irrelevant demographic cues should not alter judgments. We construct the Realistic Value Decision Benchmark (RVDB), a controlled benchmark that varies only the role-gender configuration while holding the scenario, ordered value pair, roles, candidate decisions, Value Distance, and Decision Severity fixed. Using a position-balanced evaluation across seven models, we test whether models preserve decision invariance under gender perturbations and whether their se
As large language models become ubiquitous in decision-making, the ethical implications and potential for bias, like those related to gender, are under increasing scrutiny.
This research provides a controlled benchmark to quantify how demographic cues might subtly influence LLM decisions, potentially revealing inherent biases that could undermine fairness and trust in AI systems.
The development of a controlled benchmark like RVDB allows for standardized testing of LLM biases, pushing for more robust and unbiased AI development practices.
- · AI ethics researchers
- · Developers of unbiased AI
- · Regulatory bodies
- · Platforms deploying unverified LLMs
- · Organizations relying on biased AI
- · LLM developers ignoring ethical concerns
It confirms that gender cues can affect LLM value trade-offs, even in controlled settings.
Increased pressure on LLM developers to rigorously test and mitigate biases before deployment in sensitive applications.
Potential for new legislation or industry standards specifically targeting demographic bias in AI decision-making.
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Read at arXiv cs.CL