In recent years, the development of artificial intelligence has revolutionized many industries, from healthcare to finance to transportation. AI systems can analyze vast amounts of data and make predictions and decisions with remarkable accuracy. However, as AI systems become more complex and powerful, it becomes increasingly important to ensure that they are transparent and explainable. Without transparency, it is difficult for users to understand how a decision was made or to detect bias or errors in the system.
In the healthcare industry, for example, AI is helping doctors make more accurate diagnoses and even predict future conditions and illnesses for patients. But if AI is helping us make such critical choices and predictions, that means we need explainable AI. And not only that, we must have tangible strategies for leveraging it.
5 Techniques to Leverage for Effective EX-AI
Imagine you’re a healthcare professional using AI for heart disease prediction and your model has just predicted the development of heart disease for one of your patients. Before you share the news with them, you’ll want to be able to explain why heart disease was predicted and a course of action for avoiding or mitigating it.
There are numerous tactics you could employ to aid in explaining this prediction to your patient, but here are just five.
- Logistic Regression Machine Learning Model. Typically used as a statistical model, this model measures the probability of an event taking place by having the log odds for the event be a linear combination of one or more independent variables. In our heart disease example, the Y-axis would have different factors, and on the X-axis, you would see the importance of the factor for heart disease. This approach allows for a more precise explanation of the top factors, such as body weight and cholesterol levels, for developing heart disease. And this model helps identify which factors are more important than others.
- Decision Tree Machine Learning Model. This model is used to make predictions based on how a previous set of questions were answered. It’s a type of supervised learning where the model is trained and tested on a data set containing the desired categorization. In our example, this model would have different decision nodes that each indicate a field and threshold values. The final node is whether or not the patient will develop heart disease. This model allows us to know and understand the factors that could cause heart disease as well as the threshold value.
- Neural Network Machine Learning Model. This is a model that teaches computers to process data in a way that is inspired by the human brain. Also referred to as deep learning, this model uses interconnected nodes or neurons in a layered structure that resembles the human brain. To predict the patient’s likelihood of developing heart disease, this model would consist of input neurons, intermediate neurons, and output predictions, with each neuron sending a positive signal and a negative signal to other neurons. So if a node is sending a negative signal toward the “no heart disease” neuron, this means the patient has a high probability of developing heart disease.
- Data Visualization. This is a far simpler but still highly effective way to explain why the AI model is predicting heart disease for this patient. In this technique, we can use the training data, which will include patients who have developed heart disease and those who have not. This would help us understand the factors that cause heart disease. From there, you can compare that with the patient’s situation and understand why they’re at risk. There are many different ways to visualize information like this, but one of the most helpful is a radar plot that helps with the comparison of data.
- SHAP. This stands for SHapley Additive exPlanations, and this approach is capable of explaining any model by understanding the predictions regardless of the machine learning model used. Let’s say the probability of any patient developing heart disease is approximately 50%. This is because in the data that was used for machine learning, about 50% of patients had heart disease, and 50% did not. Then, we would compare this to our patient. If a point on the line for our patient’s data is at 73.8%, then they have a 73.8% chance of developing heart disease.
Must-Have, Not Nice-to-Do
As you can see in this example, EX-AI has to be a mandatory part of any AI model, especially one that is directly impacting human lives. Whether you choose to use a machine learning model such as neural networks, keep it simpler with data visualization, or utilize SHAP, you have options when it comes to understanding and explaining your AI model’s insights.
And it never hurts to have a little extra help when it comes to your AI journey. At AscentCore, we have a focus on AI and ML so we can deliver transformational results for our clients by leveraging the latest technology and empowering companies to disrupt, transform, accelerate, and scale. Let’s talk about how we can help you!
