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Explore How to Detect and Address Machine Learning, AI Bias

Alegion is fully aware of the potential for machine learning bias because as they produce AI training data, the company is on the lookout for biases that can influence machine learning. A new white paper from Alegion, “Four Sources of Machine Learning Bias,” explores the four sources of AI bias, and how to mitigate these challenges for your AI systems. 

4 Sources of Machine Learning Bias & How to Mitigate the Impact on AI Systems

This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. Artificial intelligence (AI) isn’t perfect. It exists as a combination of algorithms and data; bias can occur in both of these elements. When we produce AI training data, we know to look for biases that can influence machine learning.

Four Sources of Machine Learning Bias

As a company that specializes in training AI systems, we know only too well that AI systems do precisely what they are taught to do. Models are only as good as their mathematical construction and the data they are trained on. Algorithms that are biased will end up doing things that reflect that bias.