AI systems, despite their advancements, still face significant performance challenges. Studies indicate that up to 85% of AI projects fail, with poor data quality being the primary culprit. AI models rely heavily on the data they are trained on, and when that data is flawed, incomplete, or biased, the resulting outputs can be unreliable.
In industries like healthcare and finance, these failures can have serious consequences, leading to catastrophic misdiagnosed patient conditions or financial losses for businesses. Additionally, privacy violations and algorithmic bias account for more than 80% of AI failure cases, raising ethical concerns about fairness and accountability
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