Explainable artificial intelligence (XAI) is a powerful tool for answering how-and-why questions. It is a set of methods and processes that enable humans to comprehend and trust the results and output generated by machine learning algorithms. It is integrated with several of Google's products and works by analyzing machine learning predictions. As a result, artificial intelligence researchers have identified explainable artificial intelligence as a necessary feature of trustworthy AI, and explainability has experienced a recent surge in attention.
Explainable artificial intelligence explains how an artificial system makes a decision. As a result, it plays a crucial role in healthcare, manufacturing, insurance, and automobiles.
Artificial intelligence in healthcare involves using its predictions (machine translation using recurrent neural networks) to explain its decisions, including diagnosing pneumonia patients. Medical imaging (classification using a convolutional neural network) data can also be used as another example where explainable artificial intelligence is beneficial.
The main objective of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands concerning artificial systems (we call these stakeholders' desiderata) in various contexts. Therefore, explainable artificial intelligence becomes crucial for an organization when building trust and confidence when using artificial intelligence models.
Artificial intelligence improves employee decisions’ quality, effectiveness, and creativity by combining analytics and pattern prediction capabilities with human intelligence. As a result, it improves decision-making and accuracy.
Other benefits of explainable artificial intelligence include:
Explainability is how the feature values of an instance are related to its model prediction so that humans understand the relationship. It answers the question, “why does this happen?”. Furthermore, it has to do with the capability of the parameters, often hidden in deep nets, to justify the results.
Interpretability is the amount of accurately predicting a model’s outcome without knowing the reasons behind the scene. The interpretability of a machine learning model makes it easier to understand the reasoning behind certain decisions or predictions. In essence, interpretability refers to the accuracy with which a machine learning model can link cause and effect.
A responsible artificial intelligence solution enables companies to engender confidence and scale AI safely by designing, developing, and deploying it with good intentions to empower employees, businesses, and society. Explainable artificial intelligence is a set of processes and methods that allow humans to comprehend and trust the results and output of machine learning algorithms. Explainable artificial intelligence describes an artificial intelligence model, its expected effect, and potential biases.
Explainable artificial intelligence is post-facto. It is necessary as an after-the-fact. It is crucial to prevent mishaps when it comes to responsible artificial intelligence.