arXiv:2607.00423v1 Announce Type: new Abstract: Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skewed demographic distributions. Current debiasing methods apply uniform bias corrections across all input queries regardless of their bias sensitivity, creating a fundamental fairness--utility trade-off. Strong debiasing distorts semantically meaningful information in bias-insensitive queries, while weak debiasing fails to mitigate stereotypes in bias-sensitive ones. This one-size-fits-all approach
The proliferation of powerful vision language models (VLMs) and their deployment in real-world applications highlights an urgent need for effective bias mitigation strategies.
Societal acceptance and regulatory compliance of AI systems, particularly VLMs, depend on addressing inherent biases that can perpetuate harmful stereotypes and lead to inequitable outcomes.
The proposed 'selective debiasing' method aims to move beyond one-size-fits-all bias correction, potentially offering a more nuanced and context-aware approach to VLM fairness.
- · AI ethicists
- · Developers of fair AI systems
- · Users of VLM applications
- · Developers of unmitigated biased AI models
- · Organizations deploying non-debiased VLMs
More accurate and contextually appropriate debiasing methods for 'person-centric' queries in VLMs will emerge.
Increased public and regulatory trust in AI systems that demonstrate verifiable and nuanced bias mitigation.
The development of a new class of 'bias-aware' AI architectures that inherently incorporate selective debiasing mechanisms, reducing the need for post-hoc corrections.
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Read at arXiv cs.CL