Bias and Variances

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Two terms of bias in Artificial Intelligence:

  1. Node Bias - The activation of a node in a neural network is determined by the following: output = activation function (dot_product(weights, inputs) + bias) This means when calculating the output of a node, the inputs are multiplied by weights, and a bias value is added to the result. The bias value allows the activation function to be shifted to the left or right, to better fit the data. Hence changes to the weights alter the steepness of the sigmoid curve, whilst the bias offsets it, shifting the entire curve so it fits better. Note also how the bias only influences the output values, it doesn’t interact with the actual input data. Glossary of Deep Learning: Bias | Jaron Collis - Deeper Learning
  2. Model Bias - Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. What Do We Do About the Biases in AI? | J. Manyika, J. Silberg, and B. Presten - Harvard Business Review

Node Bias

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When reading up on artificial neural networks, you may have come across the term “bias.” It’s sometimes just referred to as bias. Other times you may see it referenced as bias nodes, bias neurons, or bias units within a neural network. We’re going to break this bias down and see what it’s all about. We’ll first start out by discussing the most obvious question of, well, what is bias in an artificial neural network? We’ll then see, within a network, how bias is implemented. Then, to hit the point home, we’ll explore a simple example to illustrate the impact that bias has when introduced to a neural network.

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Model Bias

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