Journal of Data and Information Quality

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare but important errors—most importantly, cases for which the ...

Organizations that develop and use technologies around information retrieval, machine learning, recommender systems, and natural language processing depend on labels for engineering and experimentation. These labels, usually gathered via human ...