#GraphCast: Why You Should Be Thinking About Bias in AI

This week I'd like to point you to a cool video on algorithmic bias in AI, as machine learning depends heavily on the data it's trained on and this data can contain biases that affect its performance. Bias can come from unrepresentative or flawed training data, such as Amazon's recruiting tool which learned to discriminate against female candidates due to an industry dominated by males. Tracking data lineage in a knowledge graph is one of the ways to mitigate bias and also helps with being more ethical, especially when you don't know where your data came from.

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