Explainable AI
Alternatively known as interpretable AI, explainable AI is a set of artificial intelligence decisions easily understood by humans. Normally, AI (artificial intelligence) systems take a huge amount of data, analyze it, and make decisions without providing any human-readable output. However, explainable AI helps us analyze predictions and understand AI systems' decisions by displaying them using graphs, descriptions, and paths.
The trademark of modern businesses is transparency. Every project is unique and requires a different level of transparency. However, every system whose decisions can have a considerable impact must justify them with an explanation, and that's where explainable AI comes in.
Why is explainable I important?
It's not important for small systems like chatbots if the AI doesn't provide any clarification. However, for systems that significantly impact humans, such as drones, autonomous vehicles, and military applications, we need to understand the decision-making processes.
Using explainable AI, we can debug and enhance the AI's performance and investigate a system's behavior, which provides insights to improve the system's architecture.
How explainable AI develops trust
The main objective of AI systems is to assist humans in making better decisions. AI systems use complex methods to solve difficult tasks and make predictions, but how can we understand those predictions' real value if we cannot understand the philosophy behind them. Humans can trust AI systems' decisions with clear explanations about their strategies to come up with the suggestion. These explanatory capabilities develop trust in users that they now can interpret why certain conclusions were made.
Examples of explainable AI in different industries
Healthcare
Explainable AI can save time for medical staff by analyzing data, reaching a conclusion, and explaining that decision, allowing doctors to concentrate on medicine's interpretive tasks. Instead of doing repetitive tasks, doctors could examine more patients with great attention.
Manufacturing
When identifying and repairing equipment failures, technicians often depend on tribal knowledge, but the problem is that the tribe can change. However, explainable AI can analyze maintenance standards, equipment manuals, sensor readings, etc., to provide the best suggestion of prescriptive guide a field technician should follow.
Autonomous vehicles
Explainable AI is a critical part of the emerging arena of autonomous vehicles. With self-driving vehicles, AI enables them to handle complex situations in traffic. If the system makes any mistake, we need to know why it happened to fix the problem. But, without a proper explanation of the data results, we cannot.
Insurance
It is essential for insurance carriers to have as much knowledge as possible when making decisions. AI is getting more complex, and people want to understand how the system made its decision. Using explainable AI, people in the insurance industry can analyze the machine-made results and decide whether to adopt the decision or not. When the conclusion has justifications, humans are more likely to take action accordingly.