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Beat the Machine: Challenging Humans to Find a Predictive Model's “Unknown Unknowns”
Joshua Attenberg, Panos Ipeirotis, Foster Provost
Article No.: 1
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...
Challenges with Label Quality for Supervised Learning
Article No.: 2
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...
Information Quality Research Challenge: Adapting Information Quality Principles to User-Generated Content
Roman Lukyanenko, Jeffrey Parsons
Article No.: 3