Excited to announce our #NAACL2025 Oral paper! 🎉✨ We carried out the largest systematic study so far to map the links between upstream choices, intrinsic bias, and downstream zero-shot performance across 131 CLIP Vision-language encoders, 26 datasets, and 55 architectures!
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🚨 Key takeaway: Unwanted associations in Vision-language encoders are deeply rooted in the pretraining data and how it is curated and careful reconsideration of these methods is necessary to ensure that fairness concerns are properly addressed.
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🔧 Our analysis of intrinsic bias is carried out with a more grounded and improved version of the Embedding Association Tests with controlled stimuli (NRC-VAD, OASIS). We reduced measurement variance by 4.8% and saw ~80% alignment with human stereotypes in 3.4K tests.
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We sought to answer some pressing questions on the relationship between bias and model design choices and performance👇
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1. Upstream factors:  How do dataset, architecture, and size affect intrinsic bias? 2. Performance link : Does better zero-shot accuracy come with more bias? 3. Modality: Do images and text encode prejudice differently?
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📌 Data is key: We find that the choice of pre-training dataset is the strongest predictor of associations, over and above architectural variations, dataset size & number of model parameters.
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⚠️ What data is "high" quality? Pretraining data curated through automated or heuristic-based data filtering methods to ensure high downstream zero-shot performance (e.g. DFN, Commonpool, Datacomp) tend to exhibit the most bias!
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📊 Bias and downstream performance are linked: We find that intrinsic biases are consistently correlated with downstream task performance on the VTAB+ benchmark (r ≈ 0.3–0.8). Improved performance in CLIP models comes at the cost of skewing stereotypes in particular directions.
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🖼️ ↔️ 📝 Modality shifts biases: Cross-modal analysis reveals modality-specific biases, e.g. image-based 'Age/Valence' tests exhibit differences in bias directions; pointing to the need for vision-language alignment, measurement, and mitigation methods.
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