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.<n>Our method uncovers novel model biases in multiple image benchmark datasets.<n>The discovered bias can be mitigated by simple data re-weighting to de-correlate the features.
• Score: 15.683471433842492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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