  With the recent trend of applying machine learning in every aspect of human
life, it is important to incorporate fairness into the core of the predictive
algorithms. We address the problem of predicting the quality of public speeches
while being fair with respect to sensitive attributes of the speakers, e.g.
gender and race. We use the TED talks as an input repository of public speeches
because it consists of speakers from a diverse community and has a wide
outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and
state-of-the-art neural language models, we propose a mathematical framework
for fair prediction of the public speaking quality. We employ grounded
assumptions to construct a causal model capturing how different attributes
affect public speaking quality. This causal model contributes in generating
counterfactual data to train a fair predictive model. Our framework is general
enough to utilize any assumption within the causal model. Experimental results
show that while prediction accuracy is comparable to recent work on this
dataset, our predictions are counterfactually fair with respect to a novel
metric when compared to true data labels. The FairyTED setup not only allows
organizers to make informed and diverse selection of speakers from the
unobserved counterfactual possibilities but it also ensures that viewers and
new users are not influenced by unfair and unbalanced ratings from arbitrary
visitors to the www.ted.com website when deciding to view a talk.
