Training your Machine Learning algo to be fair, unbiased !!

Data topped with advanced algorithms like machune learning  and data mining are proving to add new revenue streams in the business world. Despite the accomplishments, data, artificial intelligence  or machine learning has achieved, it is continues to express some amount of unfairness and bias, which affects not just business but humanity at large.

If the machine algorithm is queued that a certain type of person is more likely to be commit fraud then it may end up depriving that person the resources, products or services. This is because the machine may believe that a person with a certain background and ethnicity is likely to commit fraud. Speaking on how ethics in AI have become more life-impacting, Dale Vaz, Head of Engineering & AI, Swiggy said that the choice of model matters.

Many new models like deep learning being highly sophisticated aren’t explainable. Meaning it is not unexplainable why the model made a certain choice because of the complexity of how it works. “So sometimes you might be have to go back to more explainable models which are simpler in nature and allow you to justify and substantiate on why a certain recommendation was made,” he said.

He insists on the importance of feedback on the algorithms once they are deployed to make sure it is being tested. For this, Swiggy gathers sources of inputs from its end-user. They study what their customers are telling the service agents and analyze if they are happy with it. Adding to how Lowe’s India is making its algorithms fair. Vijay Nair, Senior Director- Data Analytics, Lowe’s India, said, “We use to test and control approach no matter how we built the algorithm and how confident we are on the same.

We have a model taking a decision and a person taking the same decision to compare and see if there is any bias. If there is any bias, then we will go back to the source of data to investigate further in terms of what’s going wrong. There could be things like sampling bias that could occur so we have teams that can address the same.”

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