Artificial Intelligence (AI) has been a buzzword for quite some time now. From Siri to Alexa, AI has been a part of our daily lives. It has been used in various fields, including healthcare, finance, and transportation, to name a few. However, as AI becomes more advanced, the need for explainability becomes more critical.
Explainability refers to the ability to understand how AI makes decisions. It is essential because it helps us trust AI and its decisions. For instance, if an AI system recommends a medical treatment, we need to know how it arrived at that decision. If we don’t understand the reasoning behind the decision, we may not trust it, and we may not follow the recommended treatment.
Explainability is also essential for regulatory compliance. As AI is used in more critical applications, such as autonomous vehicles and medical diagnosis, it is subject to regulations. Regulators need to understand how AI makes decisions to ensure that it is safe and reliable.
The lack of explainability has been a significant barrier to the adoption of AI in critical applications. For instance, in healthcare, AI has the potential to improve patient outcomes by providing more accurate diagnoses and treatment recommendations. However, if doctors and patients don’t trust the AI system, they may not use it, and the potential benefits will not be realized.
The need for explainability is not new. However, as AI becomes more advanced, it becomes more challenging to explain how it makes decisions. Deep learning, a type of AI that is particularly good at recognizing patterns in data, is particularly challenging to explain. Deep learning models can have millions of parameters, making it difficult to understand how they arrive at their decisions.
Fortunately, researchers are working on developing methods for explaining AI decisions. One approach is to use visualization techniques to show how the AI system arrived at its decision. For instance, a visualization technique called saliency maps can highlight the parts of an image that the AI system used to make its decision. This can help doctors and patients understand why the AI system made a particular diagnosis.
Another approach is to use natural language explanations. This involves generating a sentence or paragraph that explains how the AI system arrived at its decision. For instance, an AI system that recommends a medical treatment could generate a sentence like “Based on your medical history and current symptoms, this treatment has been shown to be effective in similar cases.”
Explainability is not just important for critical applications like healthcare and autonomous vehicles. It is also essential for everyday applications like chatbots and recommendation systems. If we don’t understand why a chatbot or recommendation system is making a particular recommendation, we may not trust it, and we may not use it.
In conclusion, the need for explainability in AI is becoming more critical as AI becomes more advanced and is used in more critical applications. Explainability is essential for building trust in AI and ensuring regulatory compliance. Researchers are developing methods for explaining AI decisions, including visualization techniques and natural language explanations. As AI continues to advance, it is essential that we continue to prioritize explainability to ensure that we can trust AI and its decisions.