Bias is disproportionate weight in favour of or against a thing or idea usually in a prejudicial, unfair, and close-minded way. In most cases, bias is considered a negative thing because it clouds your judgement and makes you take irrational decisions.
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However, the role of bias in neural network and deep learning is much different. This article will explain the neural network bias system and how you should use it.
The Concept of Biased Data
To understand a neural network bias system, we’ll first have to understand the concept of biased data. Whenever you feed your neural network with data, it affects the model’s behaviour.
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So, if you feed your neural network with biased data, you shouldn’t expect fair results from your algorithms. Using biased data can cause your system to give very flawed and unexpected results.
For example, consider the case of Tay, a chatbot launched by Microsoft. Tay was a simple chatbot for talking to people through tweets. It was supposed to learn through the content people post on Twitter. However, we all know how Twitter can be. It destroyed Tay.
Instead of being a simple and sweet chatbot, Tay turned into an aggressive and very offensive chatbot. People were spoiling it with numerous abusive posts which fed biased data to Tay and it only learned offensive phrasings. Tay was turned off very soon after that.
Importance of Bias in Neural Network
Even though the case of Tay was very disappointing, it doesn’t mean all bias is bad. In fact, a neuron of bias in a neural network is very crucial. In neural network literature, we call them bias neurons.
A simple neural network has three kinds of neurons:
- Input Neuron
- Bias Neuron
- Output Neuron
The Input neuron simply passes the feature from the data set while the Bias neuron imitates the additional feature. We combine the Input neuron with the Bias neuron to get an Output Neuron. However, note that the additional input is always equal to 1. The Output Neuron can take inputs, process them, and generate the whole network’s output.
Let’s take the example of a linear regression model to understand a neural network bias system.
In linear regression, we have the Input neuron passing the feature (a1) and the Bias neuron mimics the same with (a0).
Both of our inputs (a1, a0) will get multiplied by their respective weights (w1, w0). As a result, we’ll get the Output Neuron as the sum of their products:
A linear regression model has i=1 and a0=1. So the mathematical representation of the model is:
y = a1w1 + w0
Now, if we remove the bias neuron, we wouldn’t have any bias input, causing our model to look like this:
y = a1w1
Notice the difference? Without the bias input, our model must go through the origin point (0,0) in the graph. The slope of our line can change but it will only rotate from the origin.
To make our model flexible, we’ll have to add the bias input, which is not related to any input. It enables the model to move up and down the graph depending on the requirements.
The primary reason why bias is required in neural networks is that, without bias weights, your model would have very limited movement when looking for a solution.
Learn More About Neural Network Bias System
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While bias is considered a bad thing in our daily life, in the world of neural networks, it’s a must-have. Without bias, your network wouldn’t give good results, as we covered in today’s article.
If you know someone who is interested in neural networks or is studying deep learning, do share this article with them.
Can the input weights be negative in neural networks?
Weights can be tuned to whatever the training algorithm decides is suitable. Since adding weights is a method used by generators to acquire the proper event density, applying them in the network should train a network that also assumes the correct event density. Actually, negative weights simply signify that increasing the given input leads the output to decrease. Thus, the input weights in neural networks can be negative.
How can we reduce bias in the neural networks of any organization?
Organizations should establish standards, regulations, and procedures for recognizing, disclosing, and mitigating any data set bias to keep bias under control. Organizations should also publish their data selection and cleansing techniques, allowing others to analyze when and if the models reflect any type of bias. However, simply ensuring that the data sets are not biased will not eliminate it completely. Therefore, having diverse teams of individuals working on AI development should remain a crucial aim for organizations.
When there is a trend in the input data, bandwagoning develops, which is a type of bias. The data confirming this tendency grows in lockstep with the trend. As a result, data scientists run the danger of exaggerating the concept in the data they gather. Furthermore, any relevance in the data might be transient: the bandwagon effect could vanish as fast as it appeared.