What is Bias?

Data can be a wonderful thing. It can identify problems, improve performance and help organizations make better decisions. However, data isn’t always representative of real life. It can be full of biases that don’t accurately reveal insights about the people it surveys. Data bias is an enormous concern for data scientists using artificial intelligence and machine learning models.

In this glossary entry, you will discover the answer to the question “What is bias in data?” and learn about the problems of overrepresented and overweighted data sets. 

Data Bias, Explained

Data bias happens when unrepresentative data sets filter into AI and machine learning models. In other words, overrepresented or heavily weighted datasets in models can lead to bias errors that don’t represent the sample of people surveyed. 

Data bias can occur for several reasons:

  • A person collecting data has a strong opinion or prejudicial views that influence the results of that data.
  • A survey or research uses a population sample that doesn’t represent the entire target group .
  • There are too many variables when a person collects data.
  • Organizations use historically-biased data to train AI and machine learning models.

When unrepresentative data goes through AI and machine learning processes, it can skew the results of data analysis and result in inaccurate insights. 

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Examples of Data Bias

Here are many famous examples of data bias. Here are two of them:

  • The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) is an algorithm used in U.S. courts to predict whether a defendant would re-offend in the future. The data model chosen to make predictions had forecasted that black defendants would be more likely to commit crimes in the future than white defendants. 
  • In 2015, an AI model used by Google to organize online photos mistakenly miscategorized black people. The AI organized photos in this prejudicial way by analyzing large amounts of biased data. 

What is Bias? Types of Data Bias

Data scientists might come across the following data bias types:

Confirmation bias

This bias happens when someone prioritizes information that confirms their beliefs over information that doesn’t. Also known as wishful thinking, confirmation bias can trickle down into data sets and models and skew analysis. 

Selection bias

Selection bias occurs when data samples don’t accurately represent the whole population or a target audience. Small data sets or inaccurate data collection methods can result in selection bias.

Availability bias

Availability bias occurs when someone bases a belief solely on the information available rather than looking at the bigger picture. Data scientists can eliminate this bias by examining larger data sets and removing outliers that might influence data analysis. 

Historical bias

This bias occurs when organizations use historically-biased information to train AI and machine learning models. That biased information can distort data analysis and produce inaccurate results. 

Overgeneralization bias

Overgeneralization can occur when someone applies something from one event to all future events. For example, an organization might presume that an outcome will happen in the future because it happened in the past. 

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Data Bias Risks

There are no benefits of data bias, only negatives. Here are some of the problems that can occur when unrepresentative data ends up in AI and machine learning models:

  • AI and machine models are ‘self-learning,’ but they learn from data sets susceptible to human biases. When raw data is unrepresentative, it can negatively influence the results of analysis and cost organizations money to rectify these issues. 
  • Data bias can produce adverse outcomes that cause offense to audiences and jeopardize an organization’s reputation. 
  • Data bias can expose prejudicial views in an organization. 

What is Bias in Data? Final Word

Data bias can result in adverse outcomes for organizations when AI and machine learning models produce inaccurate results because of overrepresented and overweighted data sets. While data scientists can never truly eliminate bias, these professionals can learn best practices to reduce it in data analysis.

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