arXiv:2606.24596v1 Announce Type: new Abstract: As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from widespread methodological fragmentation, which yields contradictory conclusions. This stems largely from ignoring the structural framing of benchmark-level evaluations. To resolve this, we introduce a unified and controllable framework that standardizes heterogeneous benchmarks to systematically contrast isolated demographic assessments with forced-choice comparative settin
The increasing deployment of Large Language Models in critical applications necessitates robust and standardized evaluation methods for social biases to ensure their responsible development.
Fragmented evaluation methodologies lead to contradictory conclusions about AI bias, hindering effective mitigation and potentially undermining public trust and regulatory efforts.
A unified framework to standardize benchmark evaluations could lead to more consistent and reliable assessments of social bias in AI, informing better design choices and policy.
- · AI developers
- · Policymakers
- · Ethical AI researchers
- · Users of critical AI applications
- · Developers ignoring bias evaluation
- · AI systems with unmitigated biases
Improved understanding and quantification of social biases in Large Language Models.
More effective and standardized regulatory frameworks for AI bias detection and mitigation.
Enhanced public trust and broader adoption of AI in sensitive applications due to reduced perceived bias risks.
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Read at arXiv cs.CL