What is explainable artificial intelligence?

Explainable artificial intelligence (XAI) is a collection of methods and processes that allow an average user to understand and trust the output and results generated by a machine learning algorithm or AI. It helps humans comprehend the reasoning behind the algorithms.

Why is explainability important?

AI is a powerful automation tool that can derive important decisions and can have negative and positive impacts on businesses. For instance, if a company develops an AI tool to automate the hiring process, there is a need to explain how the tool decides whether a candidate is eligible or not.

Many organizations want to to use AI, but are not comfortable using AI to make impactful decisions, as they cannot trust the model yet. An important goal of XAI is to provide algorithmic accountability. As AI becomes more prevalent, it becomes important to disclose how bias and questions of trust are being dealt with.

Benefits of XAI

• Trustworthy AI: XAI gives understandable explanations of the decisions made by AI to its users.

• Improved AI system: XAI brings transparency to the whole system, thus allowing developers to easily identify bugs and fix them.

• Preventing AI bias: Since XAI explains how a particular system draws a decision, it'll be easier to identify any AI bias and prevent it.

• Insights against adversarial attack: Adversarial attacks fool or misguide an ML model into making incorrect decisions. This kind of an attack on an XAI system, will reveal inconsistent explanations, with which an attack can be identified.

Applications of XAI

• Finance: It can be used to detect fraud, where it is important to explain why a particular transaction or activity is considered as fraudulent.

• Healthcare: XAI can be applied to medical diagnosis and treatment, explaining how the diagnosis was made, whichh can prove as a helpful aid for the doctors

• Legal systems: By providing explanation for legal outcomes, it can assist lawyers and judges to understand the reasoning behind decisions, which will enhance transparency and fairness.

• Social sciences and policy making: It can provide insights and explanations for complex models, it can help understand the impact of the policies.