Common-Sense Bias Modeling for Classification Tasks

• URL: http://arxiv.org/abs/2401.13213v5
• Date: Mon, 20 Jan 2025 22:21:13 GMT
• Title: Common-Sense Bias Modeling for Classification Tasks
• Authors: Miao Zhang, Zee fryer, Ben Colman, Ali Shahriyari, Gaurav Bharaj,
• Abstract summary: We propose a novel framework to extract comprehensive biases in image datasets based on textual descriptions. Our method uncovers novel model biases in multiple image benchmark datasets. The discovered bias can be mitigated by simple data re-weighting to de-correlate the features.
• Score: 15.683471433842492
• License:
• Abstract: Machine learning model bias can arise from dataset composition: correlated sensitive features can distort the downstream classification model's decision boundary and lead to performance differences along these features. Existing de-biasing works tackle the most prominent bias features, such as colors of digits or background of animals. However, real-world datasets often include a large number of feature correlations that intrinsically manifest in the data as common sense information. Such spurious visual cues can further reduce model robustness. Thus, domain practitioners desire a comprehensive understanding of correlations and the flexibility to address relevant biases. To this end, we propose a novel framework to extract comprehensive biases in image datasets based on textual descriptions, a common sense-rich modality. Specifically, features are constructed by clustering noun phrase embeddings with similar semantics. The presence of each feature across the dataset is inferred, and their co-occurrence statistics are measured, with spurious correlations optionally examined by a human-in-the-loop module. Downstream experiments show that our method uncovers novel model biases in multiple image benchmark datasets. Furthermore, the discovered bias can be mitigated by simple data re-weighting to de-correlate the features, outperforming state-of-the-art unsupervised bias mitigation methods.

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