Emotion Bias Dataset (EBD)

Vision-based cognitive services (CogS) have become crucial in a wide range of applications, from real-time security and social networks to smartphone applications. Many services focus on analyzing people images. When it comes to facial analysis, these services can be misleading or even inaccurate, raising ethical concerns such as the amplification of social stereotypes. We analyzed popular Image Tagging CogS that infer emotion from a person’s face, considering whether they perpetuate racial and gender stereotypes concerning emotion. By comparing both CogS and Human-generated descriptions on a set of controlled images, we highlight the need for transparency and fairness in CogS. In particular, we document evidence that CogS may actually be more likely than crowdworkers to perpetuate the stereotype of the “angry black man" and often attribute black race individuals with “emotions of hostility". This dataset consists of the raw data collected for this work, both from Emotion Analysis Services (EAS) and Crowdsourcing (Crowdworkers from the Appen (formerly known as FigureEight) Platform targeting US and India participants. We’ve used the Chicago Face Database (CFD) as our primary dataset for testing the behavior of the target EAS.