4 human-caused biases we need to fix for machine learning | Contributors - The Next Web

Glen Ford, Director of Product Management at Alegion argues, Bias is an overloaded word. It has multiple meanings, from mathematics to sewing to machine learning, and as a result it’s easily misinterpreted.

Photo: The Next Web

When people say an AI model is biased, they usually mean that the model is performing badly. But ironically, poor model performance is often caused by various kinds of actual bias in the data or algorithm.

Machine learning algorithms do precisely what they are taught to do and 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.

To the extent that we humans build algorithms and train them, human-sourced bias will inevitably creep into AI models. Fortunately, bias, in every sense of the word as it relates to machine learning, is well understood. It can be detected and it can be mitigated — but we need to be on our toes.

There are four distinct types of machine learning bias that we need to be aware of and guard against.
Read more...

Source: The Next Web