Top 10 Ways to Prevent AI Bias

Even with the best of intentions, some bias may slip through the system. This is where being transparent can be beneficial, as it shows users you’re aware of any systematic preferences and are actively seeking to prevent or overcome them.

Those same people are also part of the resolution. Use the data you have to hand to factor in their backgrounds and perspectives when building your model to ensure it’s trained appropriately. Gather feedback from end-users too, as they’re most likely to pick up on any unfairness or discrimination. Issuing a simple survey is enough to gauge their perception. By reviewing their experience with your AI, you can identify issues and adapt your model to meet their needs.

You may think your AI is unbiased in theory, but how does it stand up in practice? The only way to find out is to test the algorithm in a real-world setting.For example, let’s say you provide video conferencing software for large conference room setups. The system uses AI-powered facial recognition to frame meeting participants depending on who is speaking. 

Before launching the product and even after taking it to market, put it to the test. Gather your diverse team and ensure there are no discrepancies in how the software identifies different people. Regularly monitor these capabilities by reviewing results in real time, and ensure they align with client experiences and feedback, too. If you solely test your AI’s accuracy in one setting, it may skew the results.

It is important to recognize the limitations of your systems. Your data, models, and technical solutions are unlikely to eliminate the risks of unwanted bias – especially with biased data. That’s why you need to include a human in the loop! The purpose of human-in-the-loop is to achieve what neither a computer nor a human being can achieve for themselves. This strategy is typically deployed when training an algorithm, such as a computer vision model.

Data scientists and human annotators can offer feedback that enables the models to have a better understanding of what is being shown. Imagine you’re using machine learning as part of your IT risk management process, and you specifically want to train a spam filter to look for phishing attempts. You may find the system assumes misspellings means something is spam, when in fact it can just be human error. Providing constant feedback can help the system learn and enhance its performance after each run.