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.
AI exists as a combination of algorithms and data. There can be bias in both of these elements.
When we produce AI training data we know to look for the biases that can influence machine learning. In our experience there are four distinct kinds of bias that data scientists and AI developers need to be aware of and guard against. This paper offers a brief overview of these sources of bias in machine learning and suggests ways to mitigate their impact on your AI systems.
Download the new white paper from Alegion to explore machine learning bias, and how to address these challenges.
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