The concept of Artificial Intelligence (AI) involves Neural Networks (commonly referred to as ANNs), which drew inspiration from the way biological systems process information. Nonetheless, ANNs offer a superior model for statistical pattern recognition due to the lack of external constraints found in the biological systems. We will delve into the components of ANNs, particularly weight and bias, in this article.

## The Constituent Parts of Artificial Neural Networks

ANNs comprise multiple parts, some of which are listed below:

### Inputs:

Neural networks usually receive extracted characteristics from a dataset as input to generate predictions.### Weights:

The feature values have a corresponding monetary value and are crucial in training the neural network by providing it with the necessary data. They convey the importance of each feature used in the training process.### Bias:

Neural networks can shift activation functions to the left or right in two-dimensional planes using bias. More information about this process will be provided in due course.### Summing up:

As a mathematical operation, it constitutes the product of weight and biased characteristics.### Purpose of activation:

The neural network model necessitates non-linearity.

## What does “bias” imply in a Neural Network?

Bias in a neural network is the result of multiplying a constant by the sum of all the features and weights. Its function is to offset any other influences that may be present. To optimize the model, it is advantageous to invert the activation function from either the positive or negative side.

## What is the reason behind introducing bias in neural networks?

To gain an understanding of bias in neural networks, it is best to start by discussing basic neural networks that comprise a lone hidden layer.

A neural network generates the function Y=f(X) for each instance, where X and Y are vectors with independent components, i.e., feature vector and output vector, respectively. Y=f can be expressed for the given X vector (X,W), and the associated weight ‘W’.

## Enhancing neural networks through bias introduction

Given the above-mentioned scenario, a neural network’s output values can be modified by adding bias ‘b’ to the function when erroneous predictions are made. The resulting function, y = f(x) + b, contains all the predictions generated by the neural network, with an additional ‘b’ bias offset.

Incorporating another input to the neural network layer transforms the function to y = f(x1, x2). Since x1 and x2 are uncorrelated variables, it is imperative to treat the biases of each variable, namely b1 and b2, separately. This paves the way for the neural network to be presented as y = f(x1, x2) + b1 + b2.

### In a neural network, one bias is allowable per layer.

This article delves into the question of determining the uniqueness of a neural network’s bias. To demonstrate this concept, consider a network with n inputs and a feature vector X = [x1, x2,….,xn], as well as systematic errors represented by b1, b2,….,bn. For a neural network with a single hidden layer, the function Y = f(X,W) + (b1+ b2+….bn) would be calculated, where W represents a weight matrix.

There is a general consensus that the scalar product of scalers is equivalent to ‘b.’ As a result, the linear combination of (b1+ b2+….bn) can be represented by a solitary numerical value, which can be denoted as Y = f(X,W) + b. This evidences that every layer in a neural network has its own distinct bias.

The ensuing paragraph will highlight neural networks that possess multiple hidden layers.

### Integrating bias into the activation function

Incorporating the bias element into the activation function, rather than the output, could prevent the neural network neurons from being inactive when certain values are assigned to the input. This is due to the fact that making the activation function non-linear could enable the neural network’s response to the input to be more variable.

## Biases in artificial intelligence

The following biases could influence machine learning:

### Algorithmic bias

Improper construction of algorithms can introduce bias into the machine learning procedure, which can lead to a decrease in outcome precision.

### Bias in statistics

This examination advocates for the possibility of an issue with the training dataset, such as an imbalance in the distribution of data points relating to a specific class in classification tasks, or insufficient data points for algorithmic models to be effectively trained.

### Bias caused by inadequate anchoring

When the data and measurements utilized to train models are founded on subjective views as opposed to objective criteria, the accuracy of the predictions is considerably compromised. Additionally, it may be challenging to find datasets that adhere to this subjective opinion-based standard.

### Inequality in resource accessibility

Bias can arise when the modeller generates a dataset that includes data they are already acquainted with. For instance, constructing a dataset in the healthcare industry using knowledge of a particular medical condition cannot accurately predict outcomes for other medical conditions.

### An instance of the confirmation cognitive bias

When a modeller selects data that confirms their preconceived beliefs or worldview, this can cause false predictions.

### Adverse discrimination

This occurs when the modeller neglects to consider vital information during the model training phase.

## Deep Learning and Bias: A Case Analysis

A study conducted in 2019 titled “Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data” explored the probable impact of bias on deep learning algorithms employed in the healthcare domain. The analysis examines three primary bias types, namely selection bias, measurement bias, and confounding bias. Selection bias arises when the sample population does not mirror the attributes of the population of interest, while measurement bias occurs when the data collection process is flawed or incomplete. Lastly, confounding bias arises when the correlation between two variables is inaccurately represented in the data.

### Unrecognized Patients by Algorithms and Incomplete Data

Inconsistencies between datasets and machine learning algorithms may arise due to a lack of conformity in the primary data source, such as electronic health records, which may not conform to a consistent data format. This might cause the current situation to arise.### Insufficient Sample Size and Overestimation:

Bias in healthcare can be attributed to a shortage of data from an adequate number of patients. Omitting patients based on their race or ethnicity can significantly contribute to bias and discrimination. Thus, it is critical for healthcare practitioners to guarantee that the data they employ is representative of the population they serve to ensure unbiased and valid conclusions.### Resulting from Misclassification and Measurement Errors:

Bias may infiltrate the training dataset if the data is of inferior quality or if healthcare staff enter data inconsistently.

## Preventing and Alleviating Bias in Deep Learning

To reduce the impact of bias in deep learning, consider one of the following approaches:

- Choosing the right model for machine learning.
- There is a lack of proof of class imbalance in the training dataset.
- Data processing receives careful attention.
- No data loss occurs throughout the machine learning process.

In this session, we discussed the formulation of neural networks, the role of activation functions, the use of bias to minimize errors, and the probable outcomes of improperly integrating activation functions into neural networks. We also reviewed how to select the appropriate machine learning model after conducting a comprehensive data analysis and how to accurately prepare data to avoid potential model training errors. Ultimately, our aim is to ensure the precise implementation of neural networks and proper incorporation of activation functions to optimize machine learning outcomes.