As a company that specializes in training AI systems, Alegion knows that models in fact do precisely what they are taught to do.
AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on.
When things go wrong with AI it’s for one of two reasons: either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained.
AI is far from infallible. Whether it’s autonomous vehicle accidents or facial recognition mishaps, it’s tempting for the public to think that AI can’t be trusted.
Machine learning bias in one form or another is behind many algorithm and data issues. If not mitigated, bias will cause the model to behave — or misbehave — in ways that reflect the bias.
In our experience, there are four distinct kinds of machine learning bias that data scientists and AI developers need to be aware of and guard against.
Through this paper from Alegion, AI project leads and business sponsors will better understand the four distinct types of bias that can affect machine learning, and how each can be mitigated.
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