Explainable AI (XAI)

Explainable AI (XAI) is an area of research and development in the field of artificial intelligence that aims to ensure that the decisions made by machine learning models can be understood and explained by humans. The term “Explainable AI” was first coined by the Defense Advanced Research Projects Agency (DARPA) in 2016, to address the need for increased transparency and accountability in AI systems, particularly in safety-critical domains such as defense and healthcare.

One of the main uses of Explainable AI is in creating models that can be used in situations where human oversight is needed, such as in critical decision making systems like healthcare, autonomous vehicles, and legal systems. For example, In a hospital, an explainable AI model would be used to support a diagnosis of a patient’s illness, providing the doctor with an explanation of why the model made a certain diagnosis, how the diagnosis was made, and what data was used.

Another use case of XAI is in compliance with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) which requires for the transparency in the model’s decision-making process.

When implementing XAI, a good practice is to ensure that the model provides both local and global explanations. Local explanations pertain to the individual decision made by the model, while global explanations give a broader view of the model’s overall decision process. The most common approach for achieving Explainable AI is to use methods like LIME, SHAP, and rule-based systems.

Another good practice for implement XAI is to be aware of bias and fairness in the model, this is due to the fact that these models are often used to make decisions that can affect people’s lives. For example, bias in a model used to predict the risk of recidivism in the criminal justice system can be harmful to certain groups, therefore it is important to ensure that these models are tested for bias and fairness before being deployed.

It’s important to note that the concept of XAI is still in its early stage, and there is ongoing research and development work in the field. Therefore, many of the best practices and methods for Explainable AI are still in development and evolving.