How-to-Avoid-Data-Bias

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How-to-Avoid-Data-Bias

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# How-to-Avoid-Data-Bias

• The prevention of data bias in machine learning projects is an 
ongoing process. 
• Though it is sometimes difficult to know when your machine 
learning algorithm, data or model is biased, there are a number of 
steps you can take to help prevent bias or catch it early.

• To the best of your ability, research your users in advance. Be 
aware of your general use-cases and potential outliers.

![image](https://github.com/user-attachments/assets/1c6e88f5-d147-4c7d-80bc-cea40a2c964e)

• Enlist the help of someone with domain expertise to review your 
collected and/or annotated data. Someone from outside of your 
team may see biases that your team has overlooked.

• Ensure your team of data scientists and data labellers is diverse.

![image](https://github.com/user-attachments/assets/26ad984a-b0f4-48cd-9e40-01766f8e0ad8)

• Where possible, combine inputs from multiple sources to ensure 
data diversity.

• Create a gold standard for your data labelling. A gold standard is a 
set of data that reflects the ideal labelled data for your task. It 
enables you to measure your team’s annotations for accuracy.

![image](https://github.com/user-attachments/assets/5cce6e3e-3e0b-4a04-8d3b-d7d0b60b932c)

• Make clear guidelines for data labelling expectations so data 
labellers are consistent.

• Use multi-pass annotation for any project where data accuracy 
may be prone to bias. Examples of this include sentiment analysis, 
content moderation, and intent recognition.

![image](https://github.com/user-attachments/assets/df0626ae-b2c2-4f7e-9881-4f7b8e9f9cb4)

• Analyse your data regularly. Keep track of errors and problem areas so you can respond to and resolve them quickly.

• Carefully analyse data points before making the decision to delete or keep 
them.

• Make bias testing a part of your development cycle.

![image](https://github.com/user-attachments/assets/7351136f-fd87-4292-9e46-4a8e8fe3a780)

## Bias in Dataset

• Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others.

• A biased dataset does not accurately represent a model's use case, resulting in skewed outcomes, low accuracy levels, and analytical errors.

• In general, training data for machine learning projects has to be representative of the real world.

• This is important because this data is how the machine learns to do its job.

• Data bias can occur in a range of areas, from human reporting and selection bias to algorithmic and interpretation bias.

## Sample Bias

• Sample bias occurs when a dataset does not reflect the realities of the environment in which a model will run.

• An example of this is certain facial recognition systems trained primarily on images of white men.

• These models have considerably lower levels of accuracy with women and people of different ethnicities.

• Another name for this bias is selection bias.

## Exclusion Bias

• Exclusion bias is most common at the data pre-processing stage. Most often it's a case of deleting valuable data thought to be unimportant.

• However, it can also occur due to the systematic exclusion of certain information. For example, imagine you have a dataset of customer sales in America and Canada. 98% of the customers are from America, so you choose to delete the location data thinking it is irrelevant.

• However, this means you model will not pick up on the fact that your Canadian customers spend two times more.

## Measurement Bias

• This type of bias occurs when the data collected for training differs from that collected in the real world, or when faulty measurements result in data distortion.

• A good example of this bias occurs in image recognition datasets, where the training data is collected with one type of camera, but the production data is collected with a different camera.

• Measurement bias can also occur due to inconsistent annotation during the data labelling stage of a project.

## Recall Bias

• This is a kind of measurement bias and is common at the data labelling stage of a project.

• Recall bias arises when you label similar types of data inconsistently. This results in lower accuracy.

• For example, let’s say you have a team labelling images of phones as damaged, partially-damaged, or undamaged. If someone labels one image as damaged, but a similar image as partially damaged, your data will be inconsistent.

## Observer Bias

• Also known as confirmation bias, observer bias is the effect of seeing what you expect to see or want to see in data.

• This can happen when researchers go into a project with subjective thoughts about their study, either conscious or unconscious.

• We can also see this when labellers let their subjective thoughts control their labelling habits, resulting in inaccurate data.

## Racial Bias

• Though not data bias in the traditional sense, this still warrants mentioning due to its prevalence in AI technology of late.

• Racial bias occurs when data skews in favour of demographics.

• This can be seen in facial recognition and automatic speech recognition technology which fails to recognize people of colour as accurately as it does Caucasians.

## Association Bias

• This bias occurs when the data for a machine learning model reinforces and/or multiplies a cultural bias.

• Your dataset may have a collection of jobs in which all men are doctors, and all women are nurses. This does not mean that women cannot be doctors, and men cannot be nurses.

• However, as far as your machine learning model is concerned, female 
doctors and male nurses do not exist. Association bias is best known for 
creating gender bias.

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