Algorithmic decisions are already crucially affecting our lives. The last few year, news like the ones listed below are becoming more and more common:
Twitter was in the headlines recently for apparent racial bias in the photo preview of some tweets. More specifically, Twitter’s machine learning algorithm that selects which part of an image to show in a photo preview favors showing the faces of white people over black people. For example the following tweet, contains an image of Mitch McConnell (white male) and Barack Obama (black male) twice, but Twitter selects Mitch McConnell both times in the tweet’s preview photo.
There is no doubt, that machine learning (ML) models are being used for solving several business and even social problems. Every year, ML algorithms are getting more accurate, more innovative and consequently, more applicable to a wider range of applications. From detecting cancer to banking and self-driving cars, the list of ML applications is never ending.
However, as the predictive accuracy of ML models is getting better, the explainability of such models is seemingly getting weaker. Their intricate and obscure inner structure forces us more often than not to treat them as “black-boxes”, that is, getting their predictions in a…
Software, Software Testing, Algorithms,