Alegion Outlines the 4 Most Prevalent Types of AI Bias

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AI systems are becoming more and more the norm as machine and deep learning gain ground — especially within the data center and colocation markets. That said, artificial intelligence systems are only as good as their underlying mathematics and the data they are trained on.

AI systems

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That’s according to a new white paper from Alegion that explores the bias behind machine learning.

AI systems and models are made up of algorithms and data, and the professionals who craft the models, etc., are largely in charge of underlying mathematics and data.

According to the new Alegion white paper, 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.
  • 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.

The Alegion report contends there are four different types of machine learning or AI systems bias.

  • Algorithm bias: According Alegion, it is key to remember that finding the balance between bias and variance are interdependent, and data scientists typically seek a balance between the two.
  • Sample Bias: Sample bias occurs when the data used to train the model does not accurately represent the problem space the model will operate in.
  • Prejudicial Bias: Prejudicial bias occurs when training data content is influenced by stereotypes or prejudice coming from the population.
  • Measurement Bias: This kind of bias results from faulty measurement. The outcome is a systematic distortion of all the data.

Ultimately, remember, AI models and algorithms are built by humans, which leaves room for error. AI systems are not infallible, and designers’ and programmers’ results are not always what the designers intended.

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.

From this paper 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.

Download the full report, “4 Types of Machine Learning Bias,” courtesy of Alegion, to further understand the bias behind machine learning and how to avoid four potential pitfalls.

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