This blog shares some brief thoughts on machine learning accuracy and bias.
Let’s start with some comments about a recent ALCU blog in which they run a facial recognition trial. Using Rekognition the ACLU built a face database using 25,000 publicly available arrest photos, and then performed facial similarity searches of that database using public photos of all current members of Congress. They found 28 incorrect matches out of 535, using an 80% confidence level; this is a 5% misidentification (sometimes called ‘false positive’) rate, and a 95% accuracy rate. The ACLU has not published its data set, methodology, or results in detail, so we can only go on what they’ve publicly said. But, here are some thoughts on their claims:
1. The default confidence threshold for Rekognition is 80%, which is good for a broad set of general use cases (such as identifying objects, or celebrities on social media),
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