A Taxonomy for Medical Services and Procedures Delivered via Augmented Intelligence (AI)
Until recently, there were no words to describe medical services or procedures delivered via Augmented Intelligence (AI). A working group within the AMA Digital Medicine Payment Advisory Group created the health care AI taxonomy, Appendix S, to overcome this challenge. It offers guidance for classifying various artificial intelligence/augmented intelligence (AI) applications. This guidance should be […]
Read moreA new WashPost Content Taxonomy System
The Washington Post is building a new Content Taxonomy System (CTS) to improve newsroom workflows and create increased personalization for readers. The CTS applies as many terms as needed to describe an article. Besides, articles will be tagged with the term that relates them to other articles in WashPost published in a year. These centralized, […]
Read moreTerminology, Taxonomy, and Ontology of Play, Learn and Teach Outdoors (PLaTO) Terms
Play, Learn, and Teach Outdoors (PLaTO) terms differ across languages, contexts, and cultures. Moreover, there is no clarity on how PLaTO terms are defined (i.e., terminology) and categorized (i.e., taxonomy) and how the categories are related or conceptualized (i.e., ontology). These differences and lack of cohesion make it challenging to have a harmonious dialogue across […]
Read moreDraft Taxonomy of Social Projects for Kazakhstan
The central topic of discussion during the Green Growth Forum was the draft of the Social Taxonomy for Kazakhstan. The AIFC Green Finance Centre developed and presented the draft to promote the development of the sustainable financing market. Taxonomies of sustainable finance are an important element of the sustainable finance ecosystem, preventing social washing and […]
Read moreEnhancing Artificial Intelligence through Ontologies
Artificial Intelligence (AI) aims to understand and learn the hidden relations in the data in a way that allows it to extract, reproduce and predict. The common practice is to train models on domain-limited data. While the trained model could yield excellent results in that particular domain, it is often found that the model performs […]
Read moreLeveling the Playing Field with Standardized Content Taxonomies
Earlier this year, the IAB Tech Lab announced Seller-Defined Audiences (SDA). SDA provides publishers with 1,600 IAB audience taxonomies and enables them to define audience segments using demographics, interests, and purchase-intent attributes. The objective behind SDA was to help publishers monetize their content and data with greater scale and privacy. Moreover, the scale SDA offers […]
Read moreReview of Government Financial Data Standard Launched
The audited Annual Comprehensive Financial Reports (ACFRs) are the most important and reliable information for understanding governmental entities’ fiscal health. These ACFRs are currently provided as PDF documents, severely limiting their accessibility, comparability, and usefulness for many stakeholders. Based on the eXtensible Business Reporting Language (XBRL), an open standard will modernize and digitize municipal financial […]
Read moreBuilding Explainability into Machine-Learning Models to Make Features Understandable
Explanation methods often describe how much certain features in a machine-learning model contributed to its prediction. Nevertheless, if these features were complex or convoluted, explanation methods could not help users understand them. Hence, MIT researchers are striving to improve the interpretability of features by creating a taxonomy to help developers design features that their target […]
Read moreOvercoming the Challenges in Finding Federal Data
The various agencies of the United States Department of Labor (DOL) have a vocabulary of technical terms with specific, legal meanings. Consequently, it can be difficult for people without intimate knowledge of these terms and vocabularies to efficiently discover the specific data they want. Therefore, a resource to help users understand these special terms and […]
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