As time and learning progressed, consequently IV Generation Industrial Revolution changed the way we live, work, and relate to one another. Artificial Intelligence (AI), Blockchain, Deep Learning, Machine Language, and IoT came up spreading their wings and making enterprises and companies fly with infinite horizons.

Yes, AI systems like Machine Learning and Deep Learning indeed take inputs, with no decipherable explanation or context. The AI system makes a decision and we don’t know how and why the system has arrived at that decision. This is the Blackbox model of AI which is deeply mysterious.

Explainable Artificial Intelligence (AI) meaning

Explainable AI, as the name suggests, can be, explained and understood by human brains. The outcome of Explainable AI is more reliable & trustworthy.

Simply speaking, Explainable AI is transparent in its operations so that human users can understand and trust decisions. It makes decisions cheaper, better, and faster. The goal of Explainable AI is to help people to be more productive and arrive at decisions that are reasonable and understandable. In other words, people should have an idea of why and how they are making those decisions.

Making the Blackbox of Artificial Intelligence (AI) transparent

Most of the owners, operators, users, companies, enterprises, and organizations need answers to their questions like: Why did the AI system make a specific prediction? Why did not the AI system do something else? Why should we follow the decision of AI? Is the output of AI error-free?

So, far, there is only early, seed research and work in the area of making Deep Learning approaches to Machine Learning explainable. However, it is hoped that sufficient progress can be made to arrive at the required transparency and Explainability.

According to Priti Padhy, CEO and Founder of Cognino, “I will call for a “third wave” of AI, which can adapt to novel situations and, importantly, explain its decisions. It’s no longer acceptable to just say “we don’t know how it works.” If the future is going to run on neural networks, we need to understand them now !” He emphasizes making existing AI systems transparent and explainable.

AI is an inspiring gift to mankind with new outcomes. But with the surprises comes the concern of trust. Explainable AI deals with this trust issue making the output explainable with insights. The dark black box of conventional AI systems is thus unboxed and decisions, outcomes, and predictions are made with a reasoning.

The black box of AI systems is converted into a Glass-Box by Explainable AI models. The lack of understanding of predictions made can cause large risks for companies. No matter how genius you are but if you are not able to explain to a third party how and why are you getting those wonderful predictions, then you might lose your reputation and left with nothing at all.

Explainable AI in Medicine

In fields such as Healthcare and Medicine, mistakes can have catastrophic effects, therefore the black box concept of AI makes it difficult for medical practitioners to trust the outcomes. If an AI algorithm is not trained properly, we cannot be sure that the patients will be diagnosed properly or not. Thus, xAI algorithms that are being developed for Healthcare and Medicine can provide an explanation and justification for their results.

Using advanced Natural Language Processing, Explainable AI can extract and relate some important and critical information from the previous records of the patients, giving doctors a faster approach to reach the patient’s Electronic Health Record.

One of the most important reasons for bringing Explainable AI to medicine is because the medical practitioners are working with complex, heterogeneous, complicated, and disturbed data. Therefore Explainable AI is a boon to the medical fraternity that not only organizes the unstructured data but also come out with predictions that can be explained and understood.

Explainable AI in Finance

Finance is another field that is graved with a lot of paperwork in the form of documents that are unstructured and complex, thereby making their work full of risk and uncertainty. Explainable AI comes in picture in Financial Services and Compliance by providing a directional approach to the consultants and clientele.

Explainable AI can spread its wings in Banking and Finance and extend its hands in the following departments, namely,

  • Know Your Customer (KYC)
  • AML/Fraud Management
  • Rogue Employee Detection
  • Internal Audit Compliance
  • Tax and Accounting Compliance

AI detects and correlated anomalies and forecasts compliance, threat, and performance in no real-time.

Explainable Ai in Public Sector

Artificial Intelligence has transformed several areas in the private sector, now it has started blanketing the public sector as well. Governments of all countries are always under pressure to create an efficient and effective system of citizen services.

There are various categories in this sector, and we can summarise it as:

  • Smart cities and spaces
  • Citizen services
  • Employee engagement

Explainable AI in Education

Educationalists need methods and modern techniques of AI to connect students all over the world and help them realize their potential. Challenges in the education field can be paved through solutions provided by Explainable AI.

Students, teachers, and administrators can utilize Explainable AI to enhance and improve their learning graph with improved outcomes and predictions.

AI tools like real-time language translation can overcome all language barriers and help them learn and grow. Narration of surroundings for blind students enable them to know the insights of any field.

Explainable AI in Retail Industry

The retail industry is one of the most growing concerns with increasing expectations and personalized requirements. This sector is deeply intent-driven and needs an understanding of customer needs, wants, influences and relationships.

Retailers should think more about structuring the data through a glass-box of Explainable AI. It provides a window of “why and how” of an outcome. There are also performance metrics and can track a particular output. Think of AI as another worker. All the risks involved in a business are minimized thereby, increasing revenue and customer retention.


Explainable Artificial Intelligence (AI) or Xai is an Intelligent Core Platform that makes predictions, outcomes, and decisions understandable and explainable to humans. Actions of AI are traceable to a certain level. These levels depend upon the complexity and sensitivity of consequences that may arise from an AI system. Thus, Explainable and transparent system of AI should “know everything when anything goes wrong”.

Deep down, AI has become an important and substantial part of our lives, Explainable AI has become even more important.