  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.
