Data-driven AI systems increasingly influence our choices, raising concerns about autonomy, fairness, and accountability. Achieving algorithmic autonomy requires new infrastructures, motivation ...

We use algorithms every day for things like image searches, predictive text, and securing sensitive data. Algorithms show up ...

Closing this data gap is both easy and hard. It’s easy because it has a very simple solution: collect sex-disaggregated data.

I'll explore data-related challenges, the increasing importance of a robust data strategy and considerations for businesses ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial ...

Data clustering remains an essential component of unsupervised learning, enabling the exploration and interpretation of complex datasets. The field has witnessed considerable advancements that address ...

Artificial intelligence can be a beautiful thing for business, with a lot of promise. But this promise has yet to deliver tangible results. Many AI projects fail in various stages of experimentation ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

Data structures and algorithms constitute the foundational pillars of computer science. They provide the systematic methods for organising, storing and manipulating data, and offer step-by-step ...

Social data refers to publicly shared information on social media platforms. Discover how it's used for marketing, its advantages, and the risks associated with data breaches.