Classification and AI

Artificial intelligence (AI)-driven document classification refers to the process of automatically categorizing or labeling documents based on their content using AI techniques. It involves training machine learning models to analyze the text of documents and assign them to predefined categories or classes. This interesting topic came to us from Help Net Security in their article, “OneTrust’s AI-driven document classification enhances data discovery and governance.”

AI-driven document classification has a wide range of applications, such as organizing large document collections, automating content tagging and routing, spam filtering, sentiment analysis, and many more. It can save significant time and effort by automating the manual task of sorting and categorizing documents, leading to increased efficiency and productivity in various industries.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, changing search to found.