Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

Norm Hardy’s classic Confused Deputy problem describes a privileged component that is tricked into misusing its authority on ...

In 2026, here's what you can expect from the AI industry: new architectures, smaller models, world models, reliable agents, ...

anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a ...

As digital ecosystems expand across industries, so does the threat surface they expose. For Dr. Karthik Kambhampati, a ...

Overview Machine learning offers efficiency at scale, but trust depends on understanding how decisions are madeAs machine ...

By contrasting nematode infection with drought stress, the study highlights both the potential and limitations of remote sensing for separating ...

Adnan and colleagues evaluated machine learning models’ ability to screen for Parkinson’s disease using self-recorded smile videos. 2. The models achieved high sensitivity and specificity among ...

Cognitive computing represents an innovative frontier within computer science, merging artificial intelligence, machine ...

As enterprises continue navigating the complexities of digital transformation, Rahul Jain's work reflects how thoughtful, ...

Banks and insurers must unify fragmented decisions to manage risk, regulation and customer expectations in real time.

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...