  Unbiased data collection is essential to guaranteeing fairness in artificial
intelligence models. Implicit bias, a form of behavioral conditioning that
leads us to attribute predetermined characteristics to members of certain
groups and informs the data collection process. This paper quantifies implicit
bias in viewer ratings of TEDTalks, a diverse social platform assessing social
and professional performance, in order to present the correlations of different
kinds of bias across sensitive attributes. Although the viewer ratings of these
videos should purely reflect the speaker's competence and skill, our analysis
of the ratings demonstrates the presence of overwhelming and predominant
implicit bias with respect to race and gender. In our paper, we present
strategies to detect and mitigate bias that are critical to removing unfairness
in AI.
