How To Make Sure Your Machine Learning Models Aren’t Black Boxes


How To Make Sure Your Machine Learning Models Aren’t Black Boxes Team
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One of the truisms of machine learning models is that sometimes, not even the model creator knows what is going on inside the model. It is one of the many reasons why explainable AI is now such a big focal point in the industry.

Many people see the fact that machine learning models should not be black boxes. It causes problems in a world where machine learning algorithms are being utilized to make a lot of decisions that have a real impact on how people live their lives. An ML model needs to be trained on data. However, the end result isn’t always understood completely by the team that created the model. It means you might have a model in production that no one understands how it works on the inside.

Putting Model Interpretability Into Words

The point of machine learning algorithms is to make accurate predictions based on a model. However, let’s say your machine learning algorithm makes a decision about a user’s credit rating. It denied them aloan that they desperately needed to keep a business running. How do you justify that decision to them? Could you explain what process the machine learning algorithm used to get to that decision? That is whatmodel interpretability is all about. It is about explainable AI and having an ML model that you can explain its decisions. Machine learning will be a bigger part of how we live our lives, which is why it is crucial that model developers be able to explain how AI reaches its decisions.

The What and How of Model Interpretability

Model training is something of an art and a science. Because of that, it might be difficult to figure out how the algorithm got to its decision. It is difficult putting the logic of an AI algorithm into words, which is what makes model interpretability so difficult. The AI algorithm is crunching the numbers on the data we put in, but we don’t know what exactly it crunches.

There might be other problems with implicit bias. For example, if you are training your ML model on faces, you might be biasing it towards one race or gender. Problems like this might crop up in the real world, where your machine learning models fail to perform adequately for one group of people. That will inevitably lead to claims about you being biased, which could potentially ruin your reputation. It is why transparency is such an important factor when developing machine learning models. Explainable AI is how you get to that level of transparency that people will be happy with.

Why Does Model Interpretability Matter?

Transparency will be key in a future where machine learning models are used in making important decisions that affect people’s lives. Because of this, it is critical that machine learning models don’t be black boxes. We will start seeing more machine learning algorithms in the medical field as well as finance.

The decisions these algorithms make could have profound impacts on the personal lives of many people. Explainable AI is crucial to building trust. People will not be willing to put their lives on the line if the ML model they depend on cannot be understood by everyone. The future of the industry will be shaped by how well machine learning models will fit into that model interpretability framework.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform

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