Explainable AI 5 pillars

5 Pillars of explainable AI as defined by ANAI

We have defined the pillars or the principles that an Explainable AI based model/technique should take into consideration to build great insights into the model’s workings.

Explainability on a ML model’s output has seen a major rise in interest in recent times and has been an active pursuit of many companies involved in AI and ML. Most of this interest comes from the fact that for many years ML models have been treated as complete black boxes with no clear explanations or an understanding of how the model came up with its decisions. There was a blind trust by AI researchers, data scientists, and tech people in the results the model generated if it gave an accuracy beyond a certain benchmark.

These scientists trusted the math behind the algorithms and hoped that the model learned correctly as long as the data was sampled correctly. But the stakeholders who were directly affected by the model’s results weren’t happy due to the lack of transparency within the system.

For example, Apple’s Apple Card AI system showed bias against people based on their genders such as against women, by providing different interest rates to them. The problem was with the data that was provided to the model. The model just blindly learned the systemic biases that were recorded from the past and produced results based on that.

Such biased models might lead to a bad customer experience and the creation of an unfair world where only a particular type of a person would be accepted and validated and all others are rejected. This leaves a bad taste among the users and leads to a decreased interest in such systems by stakeholders and investors further leading to the complete disuse of such systems depriving every one of many other potential benefits that they would have brought with them.

explainable AIs solve this

To solve this colossal problem and to dive deeper into how a model takes decisions, various models and techniques were researched and brought forward to give rise to the term called eXplainable AI or also known as XAI. XAI consists of numerous ways to generate explanations on a model’s output to find out features that contributed the most while making predictions and ultimately helping to detect if there are any biases present within the data and also within the model.

At ANAI, we have incorporated XAI models into our platform to help businesses detect biases and drifts within their data early on, try to reduce them at the source, and deploy models that are explainable, fair, and responsible. We define XAI based on 5 pillars- Explanation, Accuracy, Relevance, Boundaries, and Feedback. Let us dive deeper within them.


5 pillars of XAI (as defined by us)


Explainability is an essential part of the techniques provided by Explainable AI as they help uncover the hidden meaning behind an algorithm’s decision and help open the black box of ML models. Explanations generated from an AI’s output help us identify biased models and create an environment based on trust and fairness. XAI must provide evidence for all the outputs so that the stakeholders can get a clear picture of the functioning of the model.


Accuracy for an explanation is the ability of that explanation to allow different groups and individuals to understand the explanations generated by the system. Xmas can generate different explanations having different accuracies that convey the meaning to every stakeholder as simply as possible.

New methods to get accuracy for an explanation is still in the works and a part of active research. Nevertheless, an XAI system needs to generate explanations in more than one form, as a detailed explanation may be considered highly accurate but can lose out on its meaning to a certain audience.


The explanations generated by an XAI system are only meaningful if they fulfill the purpose of making the stakeholder understand the system’s decision. The XAI systems should have the ability to convey the reasoning behind an output to every single person affected by it.

But the relevance of those explanations can vary based on the explanations themselves, the person’s prior experiences, and their mental processes. Hence, XAI-based systems should also take care of this.


XAIsystems should also always know their boundaries and should not try to give out explanations if they don’t have any. Such boundaries help in creating a system that is trustable and prevents it from giving out misleading and sometimes dangerous explanations.

The proper XAI technique must be used to generate explanations for a particular use case so that the users only get explanations when they mean something and not get them just for the sake of explanations or even if there was nothing to explain.


Feedback is always an important part of any system as it keeps the system in check and helps with systematic improvement. Although not a necessity, the best XAI systems must be able to give feedback on what should be improved, along with the explanations.

Such feedback can help the model creators figure out the problems with the system easily and help in the faster development of ML models that are fair and robust.

These pillars show the five basic principles that every XAI system must follow to ensure that the explanations are readable, inclusive, and helps people in the end. Every XAI-based solution should adhere to these 5 pillars while developing and building its ML models to have a well-rounded and complete approach toward explainability and to generate reliability in such systems.

ANAI, an XAI/RAI based platform with much more

ANAI is an all-in-one ML-based solutions platform that manages the A to Z of AI, from data engineering to explainability. We offer a solution that focuses more on a no-code-based approach with a low code solution to be released in the future. ANAI’s AI engine easily outperforms and provides the best performance for any AI-based system.

Explainability is also an important aspect in ANAI’s approach with more than 25 XAI techniques/models available to generate explanations of the model’s outcome and detect biases early on so that you can deploy AI systems that are fair and robust.

Pillars of XAI, Guide to implement XAI, five pillars of explainable AI, AI Observability Pillars, Pillars for Observable Explainable AI

Connect with us for more details on XAI or any other features of ANAI at www.anai.io or email us at info@anai.io.

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