Say My Name: a Model's Bias Discovery Framework
<|reference_start|>Say My Name: a Model's Bias Discovery Framework: In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific, non-representative patterns have been raised. Many works focusing on unsupervised debiasing usually leverage the tendency of deep models to learn ``easier'' samples, for example by clustering the latent space to obtain bias pseudo-labels. However, the interpretation of such pseudo-labels is not trivial, especially for a non-expert end user, as it does not provide semantic information about the bias features. To address this issue, we introduce ``Say My Name'' (SaMyNa), the first tool to identify biases within deep models semantically. Unlike existing methods, our approach focuses on biases learned by the model. Our text-based pipeline enhances explainability and supports debiasing efforts: applicable during either training or post-hoc validation, our method can disentangle task-related information and proposes itself as a tool to analyze biases. Evaluation on traditional benchmarks demonstrates its effectiveness in detecting biases and even disclaiming them, showcasing its broad applicability for model diagnosis.<|reference_end|>
@article{ciranni2024say,
    title={Say My Name: a Model's Bias Discovery Framework},
    author={Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano, Attilio
  Fiandrotti, Vittorio Murino, Vito Paolo Pastore, Enzo Tartaglione},
    journal={arXiv preprint arXiv:2408.09570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09570},
    primaryClass={cs.LG cs.AI cs.CY}
}