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What is Explainable AI?


In the rapidly evolving world of technology, Artificial Intelligence (AI) has emerged as a groundbreaking innovation that is transforming various sectors. One of the latest advancements in this field is Explainable AI, a concept that is gaining traction due to its potential to make AI more transparent and understandable.

What Is "Explainable AI"?

Explainable AI, also known as XAI, refers to artificial intelligence systems whose actions can be easily understood by humans. Unlike traditional AI systems, which are often viewed as ‘black boxes’ due to their complex and opaque decision-making processes, Explainable AI seeks to make the decision-making process more transparent. This enables users to understand, trust, and effectively manage AI solutions.

History of "Explainable AI"

The concept of Explainable AI is not entirely new. It traces its origins back to the early days of AI development when AI systems were simple enough to be understood by their human operators. However, as AI systems became more complex, they also became less transparent. This sparked the need for Explainable AI, leading to significant research and development in this area. Today, Explainable AI is a hot topic in the field of AI research, with numerous projects underway to develop more transparent and interpretable AI systems.

Importance of "Explainable AI"

Explainable AI is crucial in the current tech landscape for several reasons. Firstly, it promotes trust in AI systems by making their decisions understandable to human users. This is particularly important in sectors such as healthcare and finance, where AI decisions can have significant real-world impacts. Secondly, Explainable AI can help to identify and correct biases in AI systems, promoting fairness and reducing the risk of discriminatory decisions. Lastly, by making AI systems more understandable, Explainable AI can help to speed up the adoption of AI technologies in various sectors.

Practical Applications

Explainable AI has numerous practical applications. In healthcare, for example, it can be used to explain the reasoning behind AI diagnoses, helping doctors to understand and trust AI-generated insights. In finance, Explainable AI can be used to provide transparent explanations for AI credit decisions, helping to ensure fairness and compliance with regulations. Other applications include autonomous vehicles, where Explainable AI can help to make sense of AI driving decisions, and cybersecurity, where it can help to explain AI threat detections.

The Role of ‘Explainable AI’ in Modern Enterprises

For modern enterprises, Explainable AI offers several benefits. It can enhance customer trust by providing transparent explanations for AI decisions, improving customer satisfaction and loyalty. It can also help to mitigate legal and regulatory risks by ensuring that AI decisions are fair, non-discriminatory, and compliant with regulations. Furthermore, by making AI systems more understandable, Explainable AI can facilitate collaboration between AI and human workers, boosting productivity and innovation.

Case Study

One example of successful implementation of Explainable AI is CEErtia, a software solution provided by OrNsoft. CEErtia uses Explainable AI to provide transparent explanations for its AI-generated insights, helping users to understand and trust its decisions. This has helped to enhance user satisfaction and trust in CEErtia, demonstrating the potential of Explainable AI in practice.

Future Outlook

Looking ahead, the future of Explainable AI looks promising. As AI systems become increasingly integrated into our lives, the demand for transparent and understandable AI is likely to grow. This could lead to further advancements in Explainable AI, making AI systems even more transparent and user-friendly. Furthermore, as more sectors adopt AI technologies, the potential applications of Explainable AI are likely to expand, offering exciting opportunities for innovation and growth.


In conclusion, Explainable AI is a transformative technology that is set to play a crucial role in the future of AI. By making AI systems more transparent and understandable, it can promote trust, fairness, and collaboration, benefiting users and businesses alike. Intrigued by the potential of AI for your business? Schedule a free consultation with us here.