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Machine Learning - How Much Can We Trust the Hand Labels?
"You can't go around saying that your data's ground truth when it's clearly riddled with bias and, like, incorrect labels and assumptions," he says. "How do we leverage them in a more robust system? How do we combined them with other sources of signal to then boost or reduce the signal introduced over all?" 'We're talking about... just like the maturity of machine learning as a whole, for the enterprise, explainable a i has been a very important thing for several years now'