Research on crowd work has often focused on task accuracy whereas other factors such as biases in data have received limited attention. We are interested in reviewing existing approaches and discussing ongoing work that helps us better understand annotation attributes contributing to biases.
An important step towards bias mitigation is detecting such biases and measuring the extent of biases in data. We seek to discuss different methods, metrics and challenges in quantifying biases, particularly in crowdsourced data. Further, we are interested in ways of comparing biases across different samples and investigating if specific biases are task-specific or task-independent.
We plan to explore novel methods that aim to reduce biases in crowd annotation in particular. Current approaches range from worker pre-selection, improving task presentation and dynamic task assignment. We seek to discuss shortcomings and limitations of existing and ongoing approaches and ideate future directions.
We want to explore how bias identification and mitigation strategies can impact the actual workers, positively or negatively. For example, workers in certain groups may face increased competition and lack of task availability. Collecting worker attributes and profiling could raise ethical concerns.