FairyTED: A Fair Rating Predictor for TED Talk Data
Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy.
['( [ITALIC] ϵ, [ITALIC] γ)', '(0.1,0.2)', '(0.5,0.2)', '0.1,0.4)', '(∞,0)']
This accuracy is obtained by training the classifier without the unfairness measure in the loss function. The mean accuracy obtained from an unfair classifier is comparable to accuracy reported in recent studies on TED talk data [cheng2014predicting, tanveer2019causality]. However, note that our language model [le2014distributed] is much simpler when compared to methods used in the cited studies. With this simple choice we emphasize the general appeal of our approach whose goal is to reduce unfair prediction in data irrespective of the embedding technique. The hyperparameter γ in the loss function plays a critical role in determining the trade-off between fairness in prediction and its accuracy, the smaller the value of γ, the more unfair the prediction.