{"id":257824,"date":"2024-06-10T14:41:51","date_gmt":"2024-06-10T14:41:51","guid":{"rendered":"https:\/\/www.techopedia.com\/?p=257824"},"modified":"2024-06-10T14:41:51","modified_gmt":"2024-06-10T14:41:51","slug":"can-synthetic-data-save-ai-from-bias-and-model-drift-expert-analysis","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/can-synthetic-data-save-ai-from-bias-and-model-drift","title":{"rendered":"Can Synthetic Data Save AI From Bias and Model Drift? Expert Analysis"},"content":{"rendered":"
Businesses and organizations around the world are rapidly settling into the harsh reality of safely deploying artificial intelligence<\/a>.<\/p>\n After barging into the global scene fueling the biggest hype in tech in the past decade, AI presented itself as a magical black box<\/a> that could do almost anything \u2014 although black box comes with some negative connotations, including not knowing what is going on inside.<\/p>\n Still, progress marches forward, and the revolution to integrate AI into operations everywhere is irresistible.<\/p>\n But companies are learning, often the hard way, that AI risks are abundant, and \u2014 when mismanaged \u2014 the risks far outweigh the benefits.<\/p>\n The biggest problems with AI? Compliance, data consent, copyright issues<\/a>, training data<\/a>, and bias<\/a>. Synthetic data<\/a> \u2014 created artificially \u2014 can mimic real-world data and could be the key to unlocking AI’s full potential. But can it save AI?<\/p>\nKey Takeaways<\/span><\/h2>\n
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