Two of the biggest questions associated with AI are “why does AI do what it does”? and “how does it do it?” Depending on the context in which the AI algorithm is used, those questions can be mere ...

As enterprises shift from AI experimentation to scaled implementation, one principle will separate hype from impact: explainability. This evolution requires implementing 'responsible AI' frameworks ...

The key to enterprise-wide AI adoption is trust. Without transparency and explainability, organizations will find it difficult to implement success-driven AI initiatives. Interpretability doesn’t just ...

NEW YORK--(BUSINESS WIRE)--Last week, leading experts from academia, industry and regulatory backgrounds gathered to discuss the legal and commercial implications of AI explainability. The industry ...

AI now touches high-stakes decisions, credit, hiring, and healthcare, yet many systems remain black boxes. Governance is lagging adoption: Recent enterprise research finds 93 percent of organizations ...

You’ve heard the maxim, “Trust, but verify.” That’s a contradiction—if you need to verify something, you don’t truly trust it. And if you can verify it, you probably don’t need trust at all! While ...

Radiation Oncology has evolved rapidly in recent decades in terms of innovations in treatment equipment, volumetric imaging, information technology and increased knowledge in cancer biology. New ...

Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...