Avoiding Garbage in Machine Learning

Anyone who works with artificial intelligence (AI) knows that the quality of the data goes a long way toward determining the quality of the result. But "garbage" is a broad and expanding category in data science – poorly labeled or inaccurate data, data that reflects underlying human prejudices, incomplete data. To paraphrase Tolstoy, great datasets are all alike, but all garbage datasets are garbage in their own, unique and horrible ways. People believe in machine learning. Israeli philosopher and historian Yuval Noah Harrari coined the term "dataism" to describe a blind faith in algorithms. This faith extends beyond machine learning's ability to analyze data.