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AI is becoming increasingly prevalent in our daily lives, and its influence is growing with the hype around generative AI. However, when it comes to trusting AI to make important decisions, the quality of the data used to drive its decisions is of utmost importance. Flawed data, whether incomplete, incorrect, or biased, can skew the accuracy of an AI’s prediction, leading to potentially catastrophic consequences. Examples of this include providing incorrect financial guidance to investors or failing to detect fraudulent transactions.
