AI 新悖论：模型越智能，数据越糟糕

Core Insights - The reliability of artificial intelligence models is increasingly compromised by the quality of underlying data, leading to a paradox where smarter models may rely on poorer data quality [2][4]. Group 1: Data Quality Issues - The industry has long believed that more data leads to deeper insights, but excessive data has become noise, undermining reliability and authenticity [3][7]. - Suppliers may use filler data or false signals to maintain scale, which erodes the integrity of the data ecosystem [3][4]. - Once poor-quality data enters a system, it becomes nearly impossible to separate it from good data, leading to significant distortions in insights [3]. Group 2: AI Paradox - AI is both a source of the problem and a potential solution; flawed training data leads to distorted insights, and AI can inadvertently amplify these issues [4][5]. - Users of AI tools like ChatGPT often experience frustration due to incorrect outputs, highlighting the impact of data quality on perceived reliability [4]. - AI can help identify anomalies in data, but the integrity of the entire data chain—from collectors to end-users—must be maintained for effective solutions [4]. Group 3: Shift in Focus - The emphasis should shift from collecting vast amounts of data to selecting key data that is verifiable and high-quality [5][7]. - Organizations often equate scale with credibility, but the real issue lies in the authenticity of the data rather than its volume [7]. Group 4: Human Factors - Changing perceptions about data quality is more challenging than altering technology; teams may resist new workflows due to fears of losing visibility or control [8]. - Smaller, more intelligent datasets can reveal deeper truths than large volumes of questionable data, provided that trust is maintained [8]. - Rebuilding trust through transparency and verification is as crucial as the algorithms themselves, as AI can magnify existing data issues [8].