American insurers are being urged not to drag their feet on ensuring their use of AI is “explainable” to regulators and consumers.

As increasing use cases of AI in insurance add urgency to the need for explainability and transparency, experts are recommending "explainable AI" best practices to follow and key challenges to ...

Explainable AI is used throughout the credit process: Risk Assessment: Helping banks identify potential default risks with ...

For example, consider an AI that assists doctors ... This increase in transparency can prove to be the key to achieving explainable AI, something which is necessary for AI adoption in stagnant ...

Transparency Concerns: AI’s black-box nature raises questions about how decisions are made, underscoring the need for explainable AI ... to prevent breaches. For example, AI tools could soon ...

Without explainable AI, enterprises risk making decisions based on unreliable or misunderstood information, potentially resulting in costly errors and loss of stakeholder trust, according to Frost.

Generative AI is reshaping software development by automating tasks such as code generation, bug detection, and testing automation. According to IBM, generative AI is used extensively in software ...

As a result, AI images can showcase forms of discrimination. For example, most AI images of prestigious jobs will automatically feature White males. Sometimes, AI images will incorporate things ...