New scientific approach reduces bias in training data for improved machine learning

As companies and decision-makers increasingly look to machine learning to make sense of large amounts of data, ensuring the quality of training data used in machine learning problems is becoming critical. That data is coded and labeled by human data annotators—often hired from online crowdsourcing platforms—which raises concerns that data annotators inadvertently introduce bias into the process, ultimately reducing the credibility of the machine learning application's output.

from News on Artificial Intelligence and Machine Learning https://ift.tt/2WEtsmb

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