Opening the Black Box: Making AI More Explainable

Many AI systems, especially those based on deep learning, are often described as “black boxes.” This means that even the developers who created them may not fully understand how they arrive at their decisions.

But researchers are working on ways to make AI more explainable. This involves developing techniques to visualize how AI systems process information and identify the factors that contribute to their outputs.

Imagine an AI system used to approve loan applications. An explainable AI system could show you which factors, such as your credit score, income, and employment history, were most important in the decision-making process.

Explainable AI is crucial for building trust, ensuring fairness, and identifying potential biases in AI systems. It also allows us to learn from AI and gain a deeper understanding of how it works.