Making Fairness an Intrinsic Part of Machine Learning

The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc is considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science community has been ignoring so far? The answer is yes — in the pursuit of accuracy, most models sacrifice “fairness” and “interpretability.” Rarely, a data scientist tries to dissect a model to find out if the model follows all ethical norms. This is where machine learning fairness and interpretability of models come into being.

[Related Article: AI Ethics: Avoiding Our Big Questions]

There have been multiple instances when an ML model was found to discriminate against a particular section of society, be it rejecting female candidates during hiring, systemically disapproving loans to working women, or having a high rejection rate for darker color candidates. Recently, it was found that facial recognition algorithms that are available as open-source have lower accuracy on female faces with darker skin color than vice versa. In another instance, research by CMU showed how a Google ad showed an ad for high-income jobs to men more often than women.

Certain people are from protected categories. For instance, if a business differentiates against a person solely due to the fact that they are a person of color, it would be considered unethical and illegal. However, some ML models in banks…