Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality
  of Public Speeches
  The role of verbal and non-verbal cues towards great public speaking has been
a topic of exploration for many decades. We identify a commonality across
present theories, the element of "variety or heterogeneity" in channels or
modes of communication (e.g. resorting to stories, scientific facts, emotional
connections, facial expressions etc.) which is essential for effectively
communicating information. We use this observation to formalize a novel
HEterogeneity Metric, HEM, that quantifies the quality of a talk both in the
verbal and non-verbal domain (transcript and facial gestures). We use TED talks
as an input repository of public speeches because it consists of speakers from
a diverse community besides having a wide outreach. We show that there is an
interesting relationship between HEM and the ratings of TED talks given to
speakers by viewers. It emphasizes that HEM inherently and successfully
represents the quality of a talk based on "variety or heterogeneity". Further,
we also discover that HEM successfully captures the prevalent bias in ratings
with respect to race and gender, that we call sensitive attributes (because
prediction based on these might result in unfair outcome). We incorporate the
HEM metric into the loss function of a neural network with the goal to reduce
unfairness in rating predictions with respect to race and gender. Our results
show that the modified loss function improves fairness in prediction without
considerably affecting prediction accuracy of the neural network. Our work ties
together a novel metric for public speeches in both verbal and non-verbal
domain with the computational power of a neural network to design a fair
prediction system for speakers.
