Opinion by: Rowan Stone, CEO at Sapien
AI is a paper tiger without human expertise in data management and training practices. Despite massive growth projections, AI innovations won’t be relevant if they continue training models based on poor-quality data.
Besides improving data standards, AI models need human intervention for contextual understanding and critical thinking to ensure ethical AI development and correct output generation.
AI has a “bad data” problem
Humans have nuanced awareness. They draw on their experiences to make inferences and logical decisions. AI models are, however, only as good as their training data.
An AI model’s accuracy doesn’t entirely depend on the underlying algorithms’ technical sophistication or the amount of data processed. Instead, accurate AI performance depends on trustworthy, high-quality data during training and analytical performance tests.
Bad data has multifold ramifications for training AI models: It generates prejudiced output and hallucinations from faulty logic, leading to lost time in retraining AI models to