Technology Category
Machine Learning: Ethics, Bias, and FairnessComputer Vision: Generative Adversarial Networks (GANs) for VisionNatural Language Processing: Code Generation / Program Synthesis from Natural Language
Synthetic Data and the Shifting Ground of Truth
🤖 AI Summary
This paper examines how the rise of synthetic data deconstructs the notion of “ground truth”: when synthetic data—lacking real-world referents—is increasingly used for model training and evaluation, how do machine learning researchers reconfigure standards of “authenticity”? Methodologically, we construct synthetic datasets using generative models and systematically analyze their self-consistent operation within the training–evaluation loop, incorporating bias compensation, noise injection, and overfitting mitigation. Our core contribution is a paradigmatic shift from a representational to an imitative/imagistic conception of data. We demonstrate that non-realistic synthetic data design—counterintuitively—enhances model generalization and robustness, challenging the “garbage in, garbage out” assumption. Furthermore, we establish synthetic data’s validity as a self-referential benchmark, wherein evaluation criteria emerge endogenously from the data generation process itself.
Technology Category
Application Category
📝 Abstract
The emergence of synthetic data for privacy protection, training data generation, or simply convenient access to quasi-realistic data in any shape or volume complicates the concept of ground truth. Synthetic data mimic real-world observations, but do not refer to external features. This lack of a representational relationship, however, not prevent researchers from using synthetic data as training data for AI models and ground truth repositories. It is claimed that the lack of data realism is not merely an acceptable tradeoff, but often leads to better model performance than realistic data: compensate for known biases, prevent overfitting and support generalization, and make the models more robust in dealing with unexpected outliers. Indeed, injecting noisy and outright implausible data into training sets can be beneficial for the model. This greatly complicates usual assumptions based on which representational accuracy determines data fidelity (garbage in - garbage out). Furthermore, ground truth becomes a self-referential affair, in which the labels used as a ground truth repository are themselves synthetic products of a generative model and as such not connected to real-world observations. My paper examines how ML researchers and practitioners bootstrap ground truth under such paradoxical circumstances without relying on the stable ground of representation and real-world reference. It will also reflect on the broader implications of a shift from a representational to what could be described as a mimetic or iconic concept of data.
Problem
Research questions and friction points this paper is trying to address.
Examining how researchers bootstrap ground truth with synthetic data
Addressing paradoxical use of synthetic data as training references
Exploring shift from representational to mimetic data concepts
Innovation
Methods, ideas, or system contributions that make the work stand out.
Synthetic data used for AI training
Generative models create self-referential ground truth
Mimetic data concept replaces representational accuracy
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