Earlier in the year, my friend Peter published an infographic about the nine major ways in which bias creeps into machine learning.
"Machine learning recognizes patterns and anomalies in data in order to make better decisions.For example, it can make predictions based on patterns in historical data. It can group people or identify individuals based on patterns of similarity and difference. It can even spot suspicious behaviour and sound the alarm. Despite the benefits, machine learning is triggering alarm bells of its own. The logic behind decisions is not obvious, raising accountability concerns. Reliance on data raises privacy concerns. Then there are worries about bias. There have been high-profile cases of machine learning discriminating unfairly, making offensive comparisons, and blithely ignoring important distinctions. So what is going on?"
This information graphic by Peter Stoyko identifies nine major ways bias can creep into the learning process.
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