Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions

Understand

From self-driving vehicles and back-flipping robots to virtual assistants who book our next appointment at the hair salon or at that restaurant for dinner - machine learning systems are becoming increasingly ubiquitous.

• The main reason for this is that these methods boast remarkable predictive capabilities.
• However, most of these models remain black boxes, meaning that it is very challenging for humans to follow and understand their intricate inner workings.
• Consequently, interpretability has suffered under this ever-increasing complexity of machine learning models.