Practical Steps to Addressing Algorithmic Bias

Any type of bias within market research can result in inaccurate data; it is a researcher’s job to mitigate and minimise the impact of bias when it presents itself, correcting the method to make sure that the results are accurate and reliable. One place you might not expect to find bias is within the supposedly mathematically pure logic of AI and automated machine learning.

Why Are We Talking About Algorithmic Bias?

‘Algorithmic bias’ is where a machine learning or AI algorithm develops the same biases as humans when it comes to collecting, categorising, producing, and interpreting data. The issue arises for a number of reasons, but the most prolific reason stems from the initial programming of the algorithm; the data used to train the algorithm is often incomplete or biased to start with, which leads to poorly calibrated models that only produce biased results.

Identifying Bias

Bias cannot be addressed if no one knows it’s there. Fortunately, researchers have come up with ways to detect if an algorithm is biased. The complexity of algorithms would make it near impossible for anyone without a software engineering degree to alter the code itself. The best way is to see what data is going in and what is coming out to identify any issues. The phrase ‘garbage in, garbage out’ is particularly applicable in this scenario.

Addressing Algorithmic Bias

With machine learning algorithm technology still at an early stage, taking steps to reduce the unwanted bias are part of a new area of research. Unfortunately, that means there is not yet a cure, so to speak, for algorithmic bias. However, having a human present to make sure every effort to be unbiased is being made is an important step.

Conclusion

Algorithmic bias means that algorithms are becoming biased based on inaccurate data that is not representative of the real world. This is a problem for many reasons, but in the world of business, the main reason is because the results will also be biased and will therefore inaccurately inform business decisions.

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