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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.01640 |
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| _version_ | 1866909060194893824 |
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| author | Yfantidou, Sofia Spathis, Dimitris Constantinides, Marios Vakali, Athena Quercia, Daniele Kawsar, Fahim |
| author_facet | Yfantidou, Sofia Spathis, Dimitris Constantinides, Marios Vakali, Athena Quercia, Daniele Kawsar, Fahim |
| contents | Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Hypothesizing that SSL models would learn more generic, hence less biased, representations, this study explores the impact of pre-training and fine-tuning strategies on fairness (i.e., performing equally on different demographic breakdowns). Motivated by human-centric applications on real-world timeseries data, we interpret inductive biases on the model, layer, and metric levels by systematically comparing SSL models to their supervised counterparts. Our findings demonstrate that SSL has the capacity to achieve performance on par with supervised methods while significantly enhancing fairness--exhibiting up to a 27% increase in fairness with a mere 1% loss in performance through self-supervision. Ultimately, this work underscores SSL's potential in human-centric computing, particularly high-stakes, data-scarce application domains like healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01640 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data Yfantidou, Sofia Spathis, Dimitris Constantinides, Marios Vakali, Athena Quercia, Daniele Kawsar, Fahim Machine Learning Computers and Society Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Hypothesizing that SSL models would learn more generic, hence less biased, representations, this study explores the impact of pre-training and fine-tuning strategies on fairness (i.e., performing equally on different demographic breakdowns). Motivated by human-centric applications on real-world timeseries data, we interpret inductive biases on the model, layer, and metric levels by systematically comparing SSL models to their supervised counterparts. Our findings demonstrate that SSL has the capacity to achieve performance on par with supervised methods while significantly enhancing fairness--exhibiting up to a 27% increase in fairness with a mere 1% loss in performance through self-supervision. Ultimately, this work underscores SSL's potential in human-centric computing, particularly high-stakes, data-scarce application domains like healthcare. |
| title | Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2401.01640 |