AI's Role in Model Improvement: Capabilities and Limitations in Fraud Detection and Beyond
Artificial intelligence (AI) models are increasingly capable of improving other AI models, particularly in constrained settings. Automated machine learning (AutoML) tools can design architectures, tune hyperparameters, and select data, often outperforming human baselines on specific tasks. Neural Architecture Search (NAS) has been used to create network structures that surpass human-designed ones, as seen with Google's NASNet. DeepMind's AlphaTensor and AlphaDev have discovered more efficient algorithms for matrix multiplication and sorting routines, respectively. However, full autonomous recursive self-improvement, where an AI system independently designs, trains, evaluates, and deploys a substantially more capable successor without human oversight, remains unproven. Experts note that while AI can assist in model development, human judgment is still crucial for framing problems, ensuring data quality, and defining safety constraints, especially in complex areas like fraud detection where false positives a...
