Synthetic Data in AI: Performance Gains versus Hallucination Risk

Abstract

The incorporation of synthetic data into AI training pipelines poses a basic paradox: although it improves model resilience and mitigates data shortages, it also increases the likelihood of hallucinations. Using a mixed-methods approach, this research rigorously analyzes this trade-off and shows that synthetic data increase hallucination rates by a factor of 4.7 while improving perturbation resistance by 23%. Spatially improbable artifacts in computer vision (17% increase), factual mistakes in natural language processing (22% of outputs), and clinically dangerous errors in healthcare (3.7% of incidences) are examples of domain-specific manifestations. The Synthetic Data Fidelity Theorem, which extends the traditional bias-variance decomposition to explicitly encompass synthetic artifact propagation, is presented to fill the theoretical gap in the knowledge of these effects. Additionally, with a prediction accuracy of R2 = 0.89, the FAITH metric system (Factuality, Alignment, Integrity Tracking for Hallucinations) is designed for real-time risk management. According to causal analysis, 23.4% of synthetic-data-induced hallucinations are caused by reward hacking and feature entanglement. Evidence suggests that hybrid data regimes (≤60% synthetic content) minimize mistakes by 41% without compromising performance, which defies the notion of universal application. To guarantee responsible deployment in crucial AI systems, the results call for a paradigm change toward domain-specific governance, backed by evidence-based recommendations for architectural choices, validation procedures, and policy frameworks.

Received: 27 June 2025 | Revised: 9 October 2025 | Accepted: 20 October 2025

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Gabriel Silva-Atencio: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.