Explainable AI

- Overview

Explainable Artificial Intelligence (XAI) is a set of processes and methods that allow human users to understand and trust the results and outputs created by machine learning (ML) algorithms.

XAI is used to describe AI models, their expected impact and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in artificial intelligence decision-making. 

XAI is critical for organizations to build trust and confidence when putting AI models into production. The explainability of AI also helps organizations adopt a responsible approach to AI development.

As AI becomes more advanced, humans are challenged to understand and trace how algorithms arrive at their results. The entire calculation process becomes what is often called an unexplainable "black box." Or how AI algorithms arrive at specific results.

There are many benefits to understanding how an AI system produces a specific output. Explainability can help developers ensure that systems work as expected, may be necessary to meet regulatory standards, or may be important for allowing people affected by a decision to question or change the outcome. 

- The Goals of Explainable AI

Explainable AI (XAI) pursues multiple goals, including transparency, causality, privacy, fairness, trust, availability, and reliability. 

In this regard, 

• Transparency helps us understand how a system makes a specific decision. 
• Causality assesses the extent to which model variables are related to each other.
• Privacy indicates whether external agents have access to the original training data.
• Fairness is an indicator for assessing the degree of bias aversion or ethical discrimination in a learning model.
• Trust is a measure of the degree of confidence in a model's performance when facing problems. 
• Usability shows the system's ability to interact safely and effectively with users; Reliability measures the consistency of learning model results under similar conditions.