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Top Characteristics of Explainable AI for Organizations to Leverage


It is well-known that artificial intelligence is reining the throne of cutting-edge technologies in multiple organizations and industries for the last few years. Every organization is instigated to leverage the smart functionalities of AI models to gain a competitive edge in the tech-driven market. But one has to keep Explainable AI or XAI in mind before completing the process of leveraging artificial intelligence in existing systems. Let us explore some of the top characteristics of Explainable AI that are important for organizations to know.


Introduction to Explainable AI

At first, organizations need to have sufficient knowledge of Explainable AI before diving into the characteristics to leverage. Explainable AI or XAI is known as a set of frameworks to help organizations understand as well as interpret predictions from AI models efficiently and effectively. Organizations can seamlessly debug and enhance the performances of these AI models and can make stakeholders understand the behavior and how these meaningful insights are generated. XAI helps to increase artificial intelligence interpretability as well as deploy AI models with the utmost trust and confidence. It is essential for organizations to gain a clear understanding of all decision-making processes without having blind faith in AI models. The management needs to comprehend the patterns of deep learning, machine learning algorithms, as well as neural networks. One of the approaches of XAI is through black-box AI models that require proper explanation through mimicking the behavioral patterns of the original models.


Four principles of XAI

Explanation: This is the topmost principle of Explanation AI that provides the capability of providing an explanation for its outcomes with proper evidence to support those outcomes. There are five types of explanations in artificial intelligence— for users, to gain trust in society, to meet regulatory and compliance requirements, developing AI models with machine learning algorithms, and for system owners.

Meaningful: This XAI principle presents that the behavior of artificial intelligence should be meaningful for stakeholders and management to understand the explanation in different ways as well as receive answers for different levels of questions

Accuracy: Accuracy is important to explain for management and stakeholders to understand how these AI models generate smart and meaningful insights into real-life problems efficiently within a short period of time

Knowledge limits: It is one of the important principles of Explainable AI that shows AI models should operate within the knowledge limits through historical as well as training data. This principle can prevent generating inappropriate insights that can lead to incurring a massive loss in the future.


Characteristics of Explainable AI

  • XAI helps to understand AI model accuracy, fairness, transparency as well as outcomes within limits in smart decision-making processes
  • One of the characteristics of Explainable AI is to build trust and confidence into the mindsets of employees and management while shifting AI models to production
  • Responsible AI comes after incorporating XAI into the existing systems of an organization
  • Explainable AI helps in promoting model auditability and productive use of artificial intelligence
  • One of the important characteristics of Explainable AI is to help in debugging or troubleshooting efficiently and effectively with a clear understanding of AI models
  • XAI helps in simplifying the entire complicated process of the decision-making functionality of artificial intelligence
  • XAI also reduces the overhead of manual inspection as well as expensive potential errors in the systems.

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