Explainable Artificial Intelligence (XAI) aims to make AI solutions transparent and understandable — from the decisions they make, to the results they generate.
As AI becomes commonplace in everything from healthcare to criminal justice, it’s important that we can trust the predictions and decisions made by these technologies. XAI’s vision is to show you why and when decisions are made as far as possible, so that actions are traceable, reliable, and compliant.
What is it?
Explainable AI (XAI) focuses on making complex AI applications understandable for everyone.
As AI grows more sophisticated, the algorithms that power it can be almost impossible to interpret. This makes it difficult to safeguard against bias, ensure outcomes are morally or ethically sound, drive trust in decisions, and guarantee compliance.
XAI tools and approaches address these challenges by making AI applications transparent and explainable for example, by identifying the probability divisions between different decisions or conclusions that were reached Generally, it’s used in areas like healthcare, criminal justice, and credit decisioning, where AI is used to make decisions that impact people’s lives, health, and economic well-being.
What’s in for you?
Being able to explain why your AI application has produced a certain outcome can ensure your users trust the decision-making process, helping drive confidence in the application.
XAI can also help you mitigate the risk of AI bias, as any issues can be spotted and addressed. This visibility also allows for better system design, as developers can find out why a system behaves in a certain way, and improve it.
Transparency and explainability are also key to proving your applications meet regulatory standards, data-handling requirements, and legal and moral expectations.
What are the trade offs?
As it’s an emerging and complex field, there’s currently no catch-all approach to making AI applications understandable. Every application and user base will require a different level of understanding, depending on the context. The techniques also only work for certain types of models and algorithms. Some techniques will allow some insight into the model's internals, but will still require interpretation.
While system developers may want technical details, regulators will need to know how data is being used. And to explain why a certain decision has been made, every factor will need to be examined, depending on the audience, context, and issue that’s occurred.
In short, it’s incredibly complex, and ‘explainable’ can mean any number of different things to different stakeholders.