Four Principles of Explainable AI

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Analytics Insight lays down a beginner’s guide to four principles of Explainable AI

Artificial Intelligence is creating cutting-edge technologies for more efficient workflow in multiple industries across the world in this tech-driven era. There are machine learning and deep learning algorithms that are too complicated for people to understand besides AI engineers or related employees. Artificial Intelligence has generated self-explaining algorithms for stakeholders and partners to comprehend the entire process of transforming enormous complex sets of real-time data into meaningful in-depth insights. This is known as Explainable Artificial Intelligence or XAI in which the results of these solutions can be easily understood by humans. It helps AI designers to explain how AI machines have generated a specific kind of insight or outcome for businesses to thrive in the market.

Multiple online courses and platforms are available for a better understanding of Explainable AI by designing interpretable and inclusive Artificial Intelligence. There are four main principles of Explainable Artificial Intelligence to interpret predictions from machine learning models. A series of models for Explainable AI is available into these categories— user-benefit, societal acceptance, regulatory and compliance, system development as well as owner benefit. Explainable AI is essential for implementing Responsible AI for AI model explainability as well as accountability.

A Brief Description: Four Principles of Explainable AI

Principles of Explainable AI are a set of four guidelines that help Explainable Artificial Intelligence to adopt some fundamental properties efficiently and effectively. The US National Institute of Standards and Technology has developed these four principles for a better understanding of how Artificial Intelligence models work. These principles apply individually and independently from each other to be evaluated in their own right.

Explanation: This is the first major principle that obligates an Artificial Intelligence model to generate a comprehensive explanation with evidence and reasoning for humans to understand the process of generating high-stakes decisions for businesses. The standards for these clear explanations are regulated by the other three principles of Explainable AI.

Meaningful: This is the second principle of Explainable AI that provides meaningful and understandable explanations to human stakeholders and partners of an organization. The more meaningful explanation, the clearer understanding of AI models. The explanations should not be complicated and need to be tailored to stakeholders, both on a group or individual level.

Explanation Accuracy: This is the third principle that orders to accurately explain and reflect the complicated process of Artificial Intelligence for producing meaningful outputs.  It helps to impose accuracy on a system’s explanations to stakeholders. There can be different explanations accuracy metrics for different groups or individuals. Thus, it is essential to provide more than one type of explanation with a 100% accuracy level.

Knowledge Limits: This is the fourth and last principle of Explainable AI which explains that the AI model only operates under specific conditions as per its design with sets of training data— the knowledge is limited for the black box. It should operate within its knowledge limits to avoid any discrepancy or unjustified outcomes for any business. The AI system is required to identify as well as declare its knowledge limits to maintain trust between an organization and its stakeholders.

Explainable AI helps to enhance AI interpretability, assess and mitigate AI risks as well as deploy AI with utmost trust and confidence. Artificial Intelligence is becoming more advanced day by day with self-explaining algorithms. It is essential for employees and stakeholders to have a clear understanding of the smart decision-making process with AI model accountability in machine learning algorithms, deep learning algorithms as well as neural networks for self-explaining algorithms.

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