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Synthetic Data in AI: Challenges, Applications, and Ethical Implications

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arxiv 2401.01629 v1 pith:ZRJGRAXT submitted 2024-01-03 cs.LG cs.AIcs.CY

classification cs.LGcs.AIcs.CY
keywords syntheticdatadatasetsethicalapplicationsbiaseschallengesimplications
verification ladder T0 review T1 audit T2 compute T3 formal
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In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant. This report delves into the multifaceted aspects of synthetic data, particularly emphasizing the challenges and potential biases these datasets may harbor. It explores the methodologies behind synthetic data generation, spanning traditional statistical models to advanced deep learning techniques, and examines their applications across diverse domains. The report also critically addresses the ethical considerations and legal implications associated with synthetic datasets, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. Synthetic CVs To Build and Test Fairness-Aware Hiring Tools

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    A new synthetic CV dataset, generated from donated real CVs, is proposed as a benchmark for fairness-aware algorithmic hiring research.

  3. Neural Restoration of Greening Defects in Historical Autochrome Photographs Based on Purely Synthetic Data

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    A modified ChaIR restoration network trained on synthetic greening defects removes green discoloration from autochrome photos, outperforming Photoshop's generative fill in qualitative tests.

  4. Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs

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    LLM-generated synthetic reviews are less diverse and sometimes more privacy-relevant than real reviews; an adaptive prompt method raises diversity metrics but does not clearly reduce privacy risk.

  5. Using Sign Language Production as Data Augmentation to enhance Sign Language Translation

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Adding synthetic sign-language data produced by stitching, a GAN, or Gaussian splatting to the training set improves sign-language translation, with the largest gains for skeleton-pose models.

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    MultiFloodSynth is a parameter-controllable synthetic flood dataset whose addition to real training data improves flood-level detection performance for YOLOv10 models.

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