With more data available to us now than ever before, how we collect and interpret that data to make decisions has come under scrutiny. It's become clear that a factor known as Data Bias can negatively impact the decisions we thought we were making purely based on facts. During an episode of the weekly Virtual30 webinar series, Director of V-Soft Labs, Manoj Iragavarapu, shares more about what Data Bias is and how to prevent it.
What is Data Bias?
As strictly a definition, Data Bias is when a set of data points don't accurately represent the real population or environment. While it may not seem like a big deal, Data Bias can cause many problems. If the data is wrong, poor decisions can be made.
For example, customer service virtual agents are trained on data points that help predict if a customer is happy or sad. If the virtual agent was given data points that say showing teeth and an upturned smile is the only way to know if someone is happy, that will exclude all people who have a neutral/resting face. This can cause agents to think that customers are mad or upset, which leads to inaccurate responses.
Types of Data Bias
There are even different types of Data Bias ranging from biases in data points to biases of opinions of the interpreter or decision maker. Watch the video above to let Manoj explain the different types of Data Bias [Timestamp: 6:36].
- Confirmation Bias
- Simpson's Paradox
- Stereotype Bias
- Modeling Bias
- Sample Bias
How to Prevent Data Bias
Now that it's understood how important it is to avoid Data Bias, there are a couple ways to prevent creating biased data. Data governance programs can be implemented to define how data is collect and used. Be proactive and strategic about ensuring all sample data is representative of the real environment. Use multiple sources of data to diversify the modeling. Make sure to define everything clearly in the collection process and get multiple people to review results. By following these steps you have less chance of creating biased data, which allows for more accurate decision making and automation processes.