How Does the Crowd Impact the Model? A Tool for Raising Awareness of Social Bias in Crowdsourced Training Data - Perikleous, Periklis; Kafkalias, Andreas; Theodosiou, Zenonas; Barlas, Pinar; Christoforou, Evgenia; Otterbacher, Jahna; Demartini, Gianluca; Lanitis, Andreas (My University, 2022)
It is increasingly easy for interested parties to play a role in the development of predictive algorithms, with a range of available tools and platforms for building datasets, as well as for training and evaluating machine learning (ML) models. For this reason, it is essential to create awareness among practitioners on the ethical challenges, such as the presence of social bias in training data. We present RECANT (Raising Awareness of Social Bias in Crowdsourced Training Data), a tool that allows users to explore the behaviors of four biometric models - predicting the gender and race, as well as the perceived attractiveness and trustworthiness, of the person depicted in an input image. These models have been trained on a crowdsourced dataset of passport-style people images, where crowd annotators described attributes of the images, and reported their own demographic characteristics. With RECANT, users can explore the correct and wrong predictions made by each model, when using different subsets of the data in training, based on annotator attributes. We present its features, along with sample exercises, as a hands-on tool for raising awareness of potential pitfalls in data practices surrounding ML.
Keywords: Algorithmic bias; Biometrics; Crowdsourcing; Data bias; Education