“Interpretability” refers to the extent to which you can understand the reasoning behind a decision. On the other hand, Machine Learning allows a computer to improve its decisions based on the data it gathers. Thus, it creates a machine learning model that keeps enhancing the code of patterns and algorithms from data. Interpretability in Machine Learning, however, means something different.
If a machine learning model has low interpretability, you won’t be able to comprehend why the machine learning model makes certain decisions and not others. On the contrary, with the high interpretability of the model, you can easily determine the reasons of a machine learning model’s decisions.
Linear and Logistic Regression, Decision Tree, etc., are some techniques of a machine learning model.
Importance of Interpretability
Why do you think it is important for a machine learning model to be interpretable?
Machine learning models have numerous applications in this digital age. The movie recommendations on streaming applications and social media page suggestions are just the tip of the iceberg. Artificial Intelligence (AI) Chatbots can conduct interviews, help online customers make a purchase decision, and power a smart vacuum cleaner that charges and works on its own.
Further employment of machine learning expands to very complex and risk-bearing scenarios. Expensive business decisions, predicting loan credibility of a candidate at a bank, drug prescriptions at a hospital, and even crime departments to analyze evidence, etc.
But how can you detect loopholes in the predictions and decisions of a machine learning model? You might want to know why a model prescribes a certain drug to a patient. Suppose you find out that a certain decision by the machine learning model is wrong or inaccurate. In that case, you’d want to know the algorithms and steps the machine utilized to produce such a result.
This is where interpretable machine learning models are effective. With proper techniques and implementation, they help you understand the reasoning of a learning model. Contrary to explainable models, interpretable models employ different techniques (Linear Regression, Logistic Regression, Decision Tree, etc.) and are fairly easy to understand.
Let’s discuss why interpretable machine learning models are so important.
Ease of Understanding
For instance, you created a model that explains how long it will take a building project to complete. The same model might also estimate how much revenue it would generate during the first few years of operation. It does so using the data that you put into the model, in addition to what it gathers from the internet (market trends, the industrial capacity of the area, income and investment statistics of the area, etc.).
And before you implement such a model into business, you need approval from senior executives. Remember, the only way they will approve a model is if they understand it.
So, interpretability will explain to your boss how the model works, using simple language rather than technical gibberish.
A model can teach you certain things
You might not have known that your working process applies a certain formula or code. An interpretable model will help you easily understand why it was used and let you learn from it. Therefore, the higher the interpretability of the model, the more you understand.
Non-Bias and Just Decisions
It is obvious that the machine learning model will make decisions based on the data it gathers and the data you put in it initially. It is not true that a model will always produce an unbiased decision. For example, due to segregation, there may be racial profiling in the processing of certain individual’s locations. Due to race, the model can make a biased decision.
However, with an interpretable model, you can determine if your model made a fair decision. Moreover, you can easily fix it and avoid such difficulties in the future.
Forecasting a Model’s Future Performance
As time goes by, the performance of a machine learning model may either improve or deteriorate. The variables it uses in its calculations or the data that it utilizes may grow obsolete or no longer be viable for its algorithms. For example, a model that predicts an individual’s gender based on the information of his or her income/ wage gap may become useless if the wage gaps in our modern society cease to exist.
For example, the model predicts individuals with income of $18,000-$20,000 as females, and individuals with income of $15,000-17,000 as male. However, on reduction of the wage gaps, it might be difficult for the model to make a decision. If the income range for men moves from 15,000-17,000 to 16,000-19,000, in this case, a model might suggest males as females.
Thus, you can predict a model’s future performance and compensate for it.
A Drawback of the Machine Learning Models
Machine Learning Models posses the susceptibility to exploitation. Users can easily manipulate results that a machine learning model produces.
For example, let’s consider a model that works to calculate the risk and credibility of loan candidates. It knows to reject candidates with multiple credit cards as they pose a high-risk on loan repayment. However, if a candidate is aware of this, he or she might cancel out all their credit cards before appearing as candidate for the loan.
In this way, they manipulate the model into showing them as suitable candidates for the loan.
Interpretability in Machine Learning allows for better understanding. It’s a feature that makes it easy for users to improve any mistakes or errors and compensate for future enhancement.
With higher interpretability, you can achieve a maximum understanding of how a machine learning model made a decision. This will allow you to back up your decisions to others by explaining to them via an interpretable model.
It is a valid belief that by knowing how a machine works and improves itself, you can improve your knowledge and understanding of intelligence. This can help you create automated models, which can further advance into better versions.