In the realm of artificial intelligence (AI), the quality of results hinges significantly on the quality of data. While considerable focus is placed on the raw data used for training AI models, the importance of metadata—the data about data—cannot be overstated. Metadata management, which involves the systematic handling of information that describes, explains and locates data, plays a crucial role in refining AI outcomes. Forbes brought this important topic to our attention in their article, “Metadata Management: A Critical Discipline That Can Help Unlock AI.“
Effective metadata management involves creating, maintaining and governing metadata to ensure data quality and usability. In the data-driven world of AI, the role of metadata management is paramount. By providing context, ensuring data quality and facilitating data integration, metadata management significantly enhances the performance and reliability of AI models. Organizations that invest in robust metadata management practices are better positioned to unlock the full potential of their data, leading to more accurate, actionable and trustworthy AI outcomes. As AI continues to evolve, the importance of metadata management will only grow, cementing its role as a cornerstone of successful AI initiatives.
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. |
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