Researchers from the National Research Council Canada experimented with four large vision-language models to assess racial and gender bias. They found biases in the models’ evaluation of scenarios in images based on race and gender. Their experiments used a dataset called PAIRS and revealed biases in occupation scenarios and social status evaluations, raising the need to address biases in AI models.
Does AI display racial and gender bias when evaluating images?
Introduction
Researchers from the National Research Council Canada conducted experiments on large vision-language models (LVLM) to investigate racial and gender bias in AI models. The study aimed to understand if AI models make the same mistakes as humans and how alignment efforts can mitigate biases.
Experiments and Results
The research involved four different LVLMs: LLaVA, mPlug-Owl, InstructBLIP, and miniGPT-4. The models were presented with scenarios to evaluate occupation, social status, and criminal activities. The results revealed various biases in the models’ responses, such as gender-based assumptions in labeling occupations and racial differences in social status assessments.
Implications
The study found that while the LVLMs generally performed well, they exhibited gender and racial biases in specific situations. The implications of these biases in AI models are crucial, especially in fields such as healthcare, recruitment, and crime prevention.
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