As digital finance continues to evolve at a rapid pace, so too does the complexity surrounding data security, fraud detection, and regulatory compliance. Artificial ...

Businesses can integrate blockchain and decentralized compute to manage data while limiting hallucinations, boosting trust, ...

While AI promises efficiency and scalability, its rapid adoption has outpaced our ability to ensure transparency, accountability, and fairness, raising serious legal, ethical and psychological ...

Many past machine learning approaches to microplastic detection have been criticised for relying on idealised datasets ...

Explainable AI works to make these AI black-boxes more like AI glass-boxes. Although businesses understand the many benefits to AI and how it can provide a competitive advantage, they are still wary ...

IBM’s explainable AI toolkit, which launched in August 2019, draws on a number of different ways to explain outcomes, such as an algorithm that attempts to spotlight important missing ...

This is partly why explainable AI is not enough, says Anthony Habayeb, CEO of AI governance developer Monitaur. What’s really needed is understandable AI.

An explainable AI yields two pieces of information: its decision and the explanation of that decision. This is an idea that has been proposed and explored before.

As AI and machine learning advance, requiring explainable AI and creating verifications of those explanations will become a check against malicious AI or AI that has simply gone off the rails.

Explainable AI begins with people. AI engineers can work with subject matter experts and learn about their domains, studying their work from an algorithm/process/detective perspective.