Empower Your algorithms with Explainable Artificial Intelligence (XAI)
Imagine that you have applied for a visa for an important business initiative.
After a long process of preparation, today is the day. With high hopes you have just begun a confident conversation with a visa officer and you are interrupted. You are handed back your passport with a rejection stamp without a line of explanation.
What would be your state of mind? You want to understand what exactly went wrong and what you should do to better prepare for the next time.
I am sorry to take you through a virtual rejection process but it was a necessary precondition to come to the point. This process may not be AI driven, but the point is: we want things important to us to make sense to us.
If there is one thing that lies at the core of human behaviour for any engagement then it is trust. If the method or process is not trustworthy then the system too will not be trusted.
Whether an outcome is manual or algorithmic, a human is bound to need a pattern to form an understanding to deal with it. If you think from a broader perspective then all human emotions and actions are results of self-explanations of the outcomes. When a person can’t leave himself without explanations, why would he let machines to get away without them, especially with unfavourable outcomes?
In the West, there are regulations like “right to explanation” and “Equal Credit Opportunity Act” already in effect. The former gives individuals a right to understand how decisions impacting their lives are made, and the latter prevents creditors from any discrimination against any applicants with regard to credit transactions on the basis of race, colour, religion, sex, age, marital status, etc.
There are three key principles of explainable Machine Learning which collectively bear the acronym: FAT.
Some corporations and researchers have rightly expanded this to add an ethical element to it and converted FAT into FATE. Ethical AI brings in the much-needed governance angle to the Machine Learning based approach.
Moreover, you might have noticed that the terms ‘Interpretability’ and ‘Explainability’ are used interchangeably. To me, Interpretability is the ability of a model to make non-subject-matter experts understand the input and output. Explainability on the other hand goes a step further and gets more into the parameters of transparency. Apart from model inference, it calls for human-friendly explanations for model’s workings as well.
The algorithm which has been the most interpretable and simplest for human understanding is undoubtedly the decision tree. The rules are extracted by traversing a decision tree from the root node to the leaf nodes. Importance features are segregated based on their contribution to the reduction in Gini index. In fact, Bayesian Rule List (BRL) by Skater is also very promising though at an experimental stage.
We also understand that all the linear models are innately interpretable. They include all the model variants of the family of Generalized Linear Models.
