Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconThe Role of Bias in Neural Networks

The Role of Bias in Neural Networks

Last updated:
1st Mar, 2021
Read Time
5 Mins
share image icon
In this article
Chevron in toc
View All
The Role of Bias in Neural Networks

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.

Best Machine Learning and AI Courses Online

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. 

Ads of upGrad blog

In-demand Machine Learning Skills

Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

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:

  1. Input Neuron
  2. Bias Neuron
  3. 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

Neural networks are aimed to imitate the functioning of the human brain and so, they have many complexities. Understanding them can be quite challenging.

The best way to study neural networks and learn about deep learning is through a machine learning and deep learning course. It will teach you the basics and advanced concepts of these fields through a structured curriculum. 

We at upGrad offer a PG Certification in Machine Learning and Deep Learning program with IIIT-B. The course lasts only for six months and is completely online. This means you can study from the comfort of your home without interrupting your professional life while taking this course. 

You will get 1:1 personalised mentorship from industry experts and more than 240 hours of learning. You must have a bachelor’s degree with 50% or equivalent passing marks to be eligible for this program. 

Also Read: Machine Learning Project Ideas

After completion, you will also get placement assistance including resume building, job opportunities portal, hiring drives and much more. Be sure to check out the course. 

Ads of upGrad blog

Popular AI and ML Blogs & Free Courses

Final Thoughts

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. 


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1Can 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.

2How 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.

Explore Free Courses

Suggested Blogs

Top 5 Natural Language Processing (NLP) Projects & Topics For Beginners [2024]
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

30 May 2024

Top 8 Exciting AWS Projects & Ideas For Beginners [2024]
AWS Projects & Topics Looking for AWS project ideas? Then you’ve come to the right place because, in this article, we’ve shared multiple AWS proj
Read More

by Pavan Vadapalli

30 May 2024

Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting
Owing to the proliferation of Machine learning applications and an increase in computing power, data scientists have inherently implemented algorithms
Read More

by Pavan Vadapalli

25 May 2024

45+ Best Machine Learning Project Ideas For Beginners [2024]
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

21 May 2024

Top 9 Python Libraries for Machine Learning in 2024
Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learn
Read More

by upGrad

19 May 2024

Top 15 IoT Interview Questions & Answers 2024 – For Beginners & Experienced
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

19 May 2024

40 Best IoT Project Ideas & Topics For Beginners 2024 [Latest]
In this article, you will learn the 40Exciting IoT Project Ideas & Topics. Take a glimpse at the project ideas listed below. Best Simple IoT Proje
Read More

by Kechit Goyal

19 May 2024

Top 22 Artificial Intelligence Project Ideas & Topics for Beginners [2024]
In this article, you will learn the 22 AI project ideas & Topics. Take a glimpse below. Best AI Project Ideas & Topics Predict Housing Price
Read More

by Pavan Vadapalli

18 May 2024

Image Segmentation Techniques [Step By Step Implementation]
What do you see first when you look at your selfie? Your face, right? You can spot your face because your brain is capable of identifying your face an
Read More

by Pavan Vadapalli

16 May 2024

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon