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Machine learning systems are only as biased as their training data and their construction. It doesn't even have to be overt. Like when Google Photos tagged phot
by cwyers 11y ago
Machine learning systems are only as biased as their training data and their construction. It doesn't even have to be overt. Like when Google Photos tagged photos of African-American users as gorillas[1] -- sure, the machine learning algorithm wasn't racist in any recognizable sense, but it reflected the bias of the training data, which didn't have enough African-American people in it to let the algorithm learn about them.
1) http://www.theverge.com/2015/7/1/8880363/google-apologizes-photos-app-tags-two-black-people-gorillas http://www.theverge.com/2015/7/1/8880363/google-apologizes-p...
- pdkl95 11y ago> training data and their construction That's what I'm talking about. Machine learning systems - and everything else that is the product of human decisions - are a reflection of the biases, intentions, and other qualities of the designer. Even the initial choice to use a machine learning system is influenced by fallible humans. Why use machine learning? What kind, and why? What is the underlying model that is being optimized? What data was it trained with? Is the design of the system a proxy for something else, intentionally or unintentionally? The real world is complicated and messy, and any model of it is always gong to be incomplete.
- eli_gottlieb 11y ago>Machine learning systems are only as biased as their training data and their construction. The Bias-Variance Tradeoff means that all machine learning involves bias. Without it, you couldn't learn.