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New research explores synthetic data generation frameworks and fairness

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the nuances of synthetic data generation (SDG) for AI models. The first paper introduces NeMo Data Designer (NDD), an open-source framework for creating multimodal synthetic data, emphasizing its extensibility and iterative design for reproducibility. The second paper delves into the concept of disparate impact in SDG, questioning whether synthetic data generation methods can inadvertently create or exacerbate biases across different groups, and proposes group-wise modeling as a potential mitigation strategy. AI

IMPACT These papers highlight advancements in creating diverse and fair synthetic datasets, crucial for training more robust and equitable AI models.

RANK_REASON Two arXiv papers discussing synthetic data generation frameworks and fairness.

Read on arXiv cs.AI →

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New research explores synthetic data generation frameworks and fairness

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Johnny Greco, Nabin Mulepati, Andre Manoel, Eric Tramel, Kirit Thadaka, Mike Knepper, Dhruv Nathawani, Dane Corneil, Yev Meyer, Alex Watson, Maarten Van Segbroeck ·

    NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation

    arXiv:2609.17699v1 Announce Type: new Abstract: We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a declarative configuration format in which human and/or a…

  2. arXiv cs.LG TIER_1 English(EN) · Paul Andrey, Micha\"el Perrot, Batiste Le Bars, Marc Tommasi ·

    Disparate Impact in Synthetic Data Generation

    arXiv:2606.13105v2 Announce Type: replace Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair …

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