A Quote by Kate Crawford
Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves.
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Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
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We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.
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Hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too - and a lot of companies either don't know how or forget to act on it.
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