What is Machine learning Bias and Where Can We See It?

Machine learning Bias

What is Machine learning Bias and Where Can We See It?

We are moving towards a tech-driven future. Artificial intelligence is becoming more prevalent in everyday life. Concern about machine learning bias is growing as machine learning and artificial intelligence become more mainstream. This topic is important because CompreFace, our face recognition software, is improving. CompreFace’s accuracy is high, at 99%. This is similar to other facial recognition systems. We are aware that the system is still susceptible to bias and we have taken steps to correct it.

While most AI biases are not intentional, they can have severe consequences for machine learning systems. Machine learning biases can cause illegal actions, lower revenue, sales, or poor customer service.

Illegal bias refers specifically to models that violate the law, such as discriminating against a particular social group. Unfair bias refers to models that have embedded unethical behavior. Imagine a model which favors women over men or other views over those of opposing views. Machine learning systems can identify inherent biases in data patterns. These biases have real-world consequences and should be addressed by AI teams.

What is Machine learning Bias?

Machine learning bias occurs When an algorithm produces biased results because of incorrect assumptions made during the machine learning process. There are many forms that can be used, including gender bias and racial bias as well as age discrimination. What is the mechanism by which bias can seep into machine learning? There are many sources available at different stages in model development.

This can lead to issues with the model’s decision-making and later, negatively impact machine learning systems. It could create a poor customer experience or misdiagnosis. It is, therefore, crucial to evaluate, monitor, and analyze ML systems in order to reduce biases that can affect decisions.

Also read: Most 6 Technologies are Needed For Machine Learning

Sources for ML Bias

It’s possible that you are wondering how bias is created and what the source of this leakage might be. Here are a few reasons machine learning systems can become infected.

1. Biased people

Machine learning algorithms reflect many biases and prejudices that humans bring to the table. Unsupervised machine learning is another problem. Humans can make predictions by labeling the data used to train a model.

2. Insufficient training data

Machine learning bias can be caused by a lack of training data from engineering teams. This results in biases within the system, as only a small amount of data is available.

3. Difficulties in de-biasing ML models

Removing sensitive attributes will not make your data fair. Numerous researches have shown that models can still be biased by removing sensitive attributes.

4. Unfair data

It is difficult to avoid bias in machine-learning models for many reasons. Consider that many decisions are made during the construction of a model, which can introduce biases that are not immediately obvious.

Spotting ML Bias

Given the many sources of bias, it is natural to become used to these discriminations, sometimes subconsciously. As the following examples show, it is possible to spot and correct biases in machine learning by being more mindful.

1. Algorithmic Gender Discrimination

We’ll start with the COMPAS algorithm. The COMPAS algorithm is used by courts to predict whether a defendant will re-offend. Based on responses to 137 surveys, the system works. The modeling and data showed that blacks are at greater risk of reoffending. On the other hand, whites were less at risk. This is an example of ML bias, which has a devastating impact on society.

2. Advertising Algorithm

Another example is Facebook’s advertising algorithm which allowed advertisers to target ads according to race, religion, or gender. Adverts for secretarial and nursing jobs were primarily targeted at women while ads for taxi drivers were targeted at mostly men. The algorithm assumed that real estate ads would bring in more.

If shown to whites, they will be of interest. They were then not made available to any other minorities. The reason was in the learning patterns of the ML system. The input data was used to create a pattern on the Facebook platform. This data reflected existing social inequalities.

3. Algorithms to Hire

The Amazon team realized in 2015 that their hiring engine didn’t like women. The resumes from the past years were used to create their machine learning algorithm. The company used ML to evaluate job applicants starting at one.

Up to five stars The system was trained to prefer men over women because most applicants were male. Amazon’s system discovered that men were more preferred than women applicants and punished resumes that included the word “women”.

These are just a few examples of ML bias’s negative outcomes. It reproduces the biases of engineers who created them, and the society in it.

Also read: Evolving Trends of Machine learning in 2022

Important Ideas to Prevent ML Bias

We must make conscious efforts to avoid ML bias. There are several ways to detect or prevent ML bias.

  • Regularly update training data to ensure that the model can learn new patterns.
  • Using data that could lead to biases (e.g. ethnic characteristics, gender, and socioeconomic status) should be avoided
  • You must ensure that your model does not have any false assumptions or biases. Collect data from multiple sources, and combine them to create a training data collection
  • Before deploying the model in real-world environments, verify and check its performance.
  • Make sure that ML teams are well-acquainted with the key features, the underlying data, and any variations.
  • Your training program should be as varied as possible and include all members of the population
  • Run bias tests to detect ML biases early in the process
  • Choose the best learning model to solve the problem

These are just a few of the key points that can be applied to eliminating bias in machine learning. For more information, see our next article about reducing bias at each stage of the machine learning project pipeline.

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