AI, as a disruptive technology, is increasingly being adopted by people and businesses alike. With its array of benefits, it is revolutionizing the business landscape. However, there are some critical biases inherent to AI that restrict its widespread adoption.
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Discover the different types of AI biases and potential solutions to them.
The biases could be the result of a fault in model development, data used for training, or something else. Either way, when it leads to incorrect and unfair outcomes, the bias is clearly present.
Understanding why these biases occur along with their impact on stakeholders is the key to resolving them.
Types of Biases
There are multiple scenarios where incorrect actions, mostly due to human error, can create biases.
Bias During Algorithm Development
A set of assumptions and criteria during model design which can introduce a bias. The different variables that are selected can represent skewed results if contextual variables are missing.
Bias Due to Improper Data
Data is perhaps the most important component of developing a properly functioning AI algorithm. There are many areas where data can be the cause of creating a bias. Some of them are:
- Low Volume of Data: Having insufficient data can lead to a data bias, as the model is trained on incomplete information.
- Using Historical Data: Even with sufficient data, if it is historical data, then it can reflect the biases in data collection. This becomes important when considering race, religion, or gender-based data.
- Sampling and Data Collection Issues: While conducting a survey, a sample of the population is used to collect data instead of the entire population. If the sample is not appropriate, then the final AI model will only be indicative of that sample and the people that fit the criteria, not the entire population.
These biases can create major problems if not properly identified and rectified.
Potential Solutions to Addressing Biases
With the right strategies, such biases can be avoided and minimized. They are:
Gathering Diverse Set of Data
It all starts with having proper data. If the database used to train the model was properly collected and is representative of the population, then the model will function better.
Additionally, all datasets should be regularly audited before they are used to train AI models to ensure there exist no biases.
Fairness Metrics and Equalization While Developing Algorithms
With proper fairness metrics established, a more equitable output can be delivered. This can be done through proper equalization and adjustment of variables while developing the model.
