Generative artificial intelligence (generative AI) has quickly become one of the most transformative technologies today, driving innovation across industries like art, entertainment, healthcare and finance. At its core, generative AI refers to systems, often using deep learning models, that can create content—whether it’s text, images, music or complex simulations—based on patterns learned from large amounts of data. But as powerful as these systems are, the quality of the data they’re trained on is key to their effectiveness, accuracy and ethical impact. CIO brought this important topic to us in their article, “Making the gen AI and data connection work.”
The accuracy of a generative AI model is directly linked to the quality of its training data. High-quality data—clean, well-organized, representative and mostly free from bias—helps the model learn accurately and produce reliable outputs. On the flip side, poor data quality can cause models to generate misleading or incorrect content, which can lead to serious problems, especially in areas like healthcare, finance or autonomous systems.
Focusing on data quality is crucial for building generative AI systems that are accurate, trustworthy, fair and ethical. The tricky part is that most organizations don’t fully understand how AI systems make decisions. That’s where explainable AI comes in. It helps users understand and trust the results generated by machine learning algorithms, making AI more transparent and reliable.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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