Sociolinguistic auto-coding has fairness problems too: measuring and mitigating bias

Sociolinguistics researchers can use sociolinguistic auto-coding (SLAC) to predict humans’ hand-codes of sociolinguistic data. While auto-coding promises opportunities for greater efficiency, like other computational methods there are inherent concerns about this method’s fairness – whether it generates equally valid predictions for different speaker groups. Unfairness would be problematic for sociolinguistic work given the central importance of correlating speaker groups to differences in variable usage. The current study examines SLAC fairness through the lens of gender fairness in auto-coding Southland New Zealand English non-prevocalic /r/. First, given that there are multiple, mutually incompatible definitions of machine learning fairness, I argue that fairness for SLAC is best captured by two definitions (overall accuracy equality and class accuracy equality) corresponding to three fairness metrics. Second, I empirically assess the extent to which SLAC is prone to unfairness; I find that a specific auto-coder described in previous literature performed poorly on all three fairness metrics. Third, to remedy these imbalances, I tested unfairness mitigation strategies on the same data; I find several strategies that reduced unfairness to virtually zero. I close by discussing what SLAC fairness means not just for auto-coding, but more broadly for how we conceptualize variation as an object of study.