Exploring responsible applications of Synthetic Data to advance Online Safety Research and Development

• URL: http://arxiv.org/abs/2402.04910v1
• Date: Wed, 7 Feb 2024 14:39:06 GMT
• Title: Exploring responsible applications of Synthetic Data to advance Online Safety Research and Development
• Authors: Pica Johansson, Jonathan Bright, Shyam Krishna, Claudia Fischer, David Leslie
• Abstract summary: The use of synthetic data provides an opportunity to accelerate online safety research and development efforts. The report explores the potential applications of synthetic data to the domain of online safety, and addresses the ethical challenges that effective use of the technology may present.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of synthetic data provides an opportunity to accelerate online safety research and development efforts while showing potential for bias mitigation, facilitating data storage and sharing, preserving privacy and reducing exposure to harmful content. However, the responsible use of synthetic data requires caution regarding anticipated risks and challenges. This short report explores the potential applications of synthetic data to the domain of online safety, and addresses the ethical challenges that effective use of the technology may present.

Related papers

• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data [104.30479583607918]
  2nd FRCSyn-onGoing challenge is based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition.
  arXiv  Detail & Related papers  (2024-12-02T11:12:01Z)
• Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data [51.41288763521186]
  Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. RAG systems may face severe privacy risks when retrieving private data. We propose using synthetic data as a privacy-preserving alternative for the retrieval data.
  arXiv  Detail & Related papers  (2024-06-20T22:53:09Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration [5.089356301032639]
  We focus on its role in safety-critical applications, introducing four types of instance-level fidelity. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues.
  arXiv  Detail & Related papers  (2024-02-10T19:45:40Z)
• Synthetic Multimodal Dataset for Empowering Safety and Well-being in Home Environments [1.747623282473278]
  This paper presents a synthetic multimodaltemporal of daily activities that fuses video data from a 3D virtual space simulator with knowledge graphs. The dataset is developed for the Knowledge Graph Reasoning Challenge Social Issues (KGRC4SI), which focuses on identifying and addressing hazardous situations in the home environment.
  arXiv  Detail & Related papers  (2024-01-26T10:05:41Z)
• Synthetic Data in AI: Challenges, Applications, and Ethical Implications [16.01404243695338]
  This report explores the multifaceted aspects of synthetic data. It emphasizes the challenges and potential biases these datasets may harbor. It also critically addresses the ethical considerations and legal implications associated with synthetic datasets.
  arXiv  Detail & Related papers  (2024-01-03T09:03:30Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• The Use of Synthetic Data to Train AI Models: Opportunities and Risks for Sustainable Development [0.6906005491572401]
  This paper investigates the policies governing the creation, utilization, and dissemination of synthetic data. A well crafted synthetic data policy must strike a balance between privacy concerns and the utility of data.
  arXiv  Detail & Related papers  (2023-08-31T23:18:53Z)
• Towards Generalizable Data Protection With Transferable Unlearnable Examples [50.628011208660645]
  We present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution.
  arXiv  Detail & Related papers  (2023-05-18T04:17:01Z)
• Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data [91.52783572568214]
  Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
  arXiv  Detail & Related papers  (2023-04-07T16:38:40Z)
• Synthetic Data: Methods, Use Cases, and Risks [11.413309528464632]
  A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. We provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.
  arXiv  Detail & Related papers  (2023-03-01T16:35:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.