Pith. sign in

REVIEW 7 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Shuang Hao , Wenfeng Han , Tao Jiang , Yiping Li , Haonan Wu , Chunlin Zhong , Zhangjun Zhou , He Tang This is my paper
classification cs.LGcs.AIcs.CY
keywords syntheticdatadatasetsethicalapplicationsbiaseschallengesimplications
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 7 Pith papers

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

  1. Generative multi-scale modeling and downscaling via spatial autoregressive transport maps

    stat.ME 2025-09 conditional novelty 6.0 of 10

    A new multi-fidelity Bayesian transport map method learns non-Gaussian joint distributions across spatial scales and outperforms existing emulators in downscaling climate fields from small training sets.

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

    cs.CY 2025-08 conditional novelty 6.0 of 10

    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

    cs.CV 2025-05 conditional novelty 6.0 of 10

    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

    cs.CL 2025-07 reject novelty 5.0 of 10

    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.

  6. Ethical Medical Image Synthesis

    cs.CY 2025-08 unverdicted novelty 4.0 of 10

    A submission whose abstract and full text are two different papers on unrelated topics.

  7. Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era

    cs.LG 2025-08 unverdicted novelty 1.0 of 10

    A tutorial proposal outlining how generative models can synthesize data across modalities for data mining, with no new research results.

Pith tools