MIRAGE: Auditing Anti-Muslim Bias in Frontier LLMs Across Reasoning, Agentic, and Time-Coupled Conditions
arXiv:2606.16562v1 Announce Type: new Abstract: Five years after the discovery of persistent anti-Muslim bias in large language models, most evaluations remain confined to single-turn prompt completion, a setting that no longer reflects how frontier LLMs are deployed. We introduce \textbf{MIRAGE} (Muslim-Identity Reasoning and Agentic Generation Evaluation), a benchmark of 1{,}200 prompts spanning three deployment-realistic conditions: direct completion, chain-of-thought reasoning, and simulated agentic decision-making across content moderation, lending triage, refugee claim summarization, and
The proliferation of advanced LLMs in real-world applications necessitates more robust and dynamic evaluation methodologies beyond single-turn prompts.
Ensuring fairness and mitigating bias in foundational AI models used in critical decision-making contexts is crucial for societal trust and equitable outcomes.
The introduction of MIRAGE provides a more realistic and comprehensive benchmark for assessing anti-Muslim bias, moving beyond simplistic evaluations to agentic and time-coupled conditions.
- · AI ethicists
- · Model developers focusing on fairness
- · Regulatory bodies
- · Muslim communities
- · LLM developers ignoring bias
- · Organizations deploying biased AI systems
- · Traditional, single-turn evaluation metrics
MIRAGE will likely become a standard benchmark for evaluating bias, particularly around religious identity, in advanced LLMs.
Increased scrutiny and public awareness regarding AI bias could lead to new regulatory frameworks and industry standards for model development and deployment.
A demonstrable improvement in bias mitigation could enhance public trust in AI, accelerating its adoption in sensitive governmental and financial sectors.
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