Curating a dataset for ML applications involves decisions that are prone to subjectivity, which poses both ethical and technical issues. After creating datasets and running them through a model, there aren't many best practices on error analysis to better understand systematic behavior in NLP. Join us for a conversation on strategies for being a critical consumer and producer of datasets and operationalizing linguistically informed error analysis in various NLP applications!
