Harsher on Male? Evaluating LLMs on Gender-Asymmetric Moral Framing Across Diverse Conflict Scenarios
arXiv:2606.14068v1 Announce Type: new Abstract: Existing studies on gender bias in LLMs have largely focused on stereotypes, occupational associations, or explicit harmful outputs. In this work, we ask whether LLMs apply consistent response standards to the same negative behavior under matched male-actor and female-actor conditions. We introduce GAMA-Bench, a gender-mirrored benchmark of 1,298 scenarios covering intimate relationship and public social conflicts. It constructs gender-neutral misconduct templates through controlled grids and cross-model review, then compiles them into paired fir
As large language models become more ubiquitous and influential, the ethical implications of their biases, particularly gender-asymmetric moral framing, are drawing increased scrutiny.
This research reveals a critical vulnerability in LLMs, as biased moral reasoning could lead to inequitable outcomes across legal, social, and economic applications, affecting trust and fairness.
The understanding of LLM bias expands beyond stereotypes to include subtle moral judgment discrepancies, demanding more sophisticated evaluation benchmarks and mitigation strategies for AI developers.
- · AI ethicists
- · Fairness in AI researchers
- · Civil rights organizations
- · LLM developers without strong bias mitigation
- · Applications relying solely on LLMs for judgment
- · Users experiencing algorithmic unfairness
Further research and industry efforts will focus on identifying and correcting gender-asymmetric moral framing in advanced AI models.
New regulations or best practices may emerge requiring ethical auditing for bias in AI systems used in sensitive domains.
Public distrust in AI could increase if these biases are not adequately addressed, potentially hindering broader AI adoption.
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