Towards Data Governance of Frontier AI Models

• URL: http://arxiv.org/abs/2412.03824v1
• Date: Thu, 05 Dec 2024 02:37:51 GMT
• Title: Towards Data Governance of Frontier AI Models
• Authors: Jason Hausenloy, Duncan McClements, Madhavendra Thakur,
• Abstract summary: We look at how data can enable new governance capacities for frontier AI models. Data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. We propose a set of policy mechanisms targeting key actors along the data supply chain.
• Score: 0.0
• License:
• Abstract: Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier data governance" opens up new avenues for monitoring and mitigating risks from advanced AI models, particularly as they scale and acquire specific dangerous capabilities. Still, frontier data governance faces challenges that stem from the fundamental properties of data itself: data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. Despite these inherent difficulties, we propose a set of policy mechanisms targeting key actors along the data supply chain, including data producers, aggregators, model developers, and data vendors. We provide a brief overview of 15 governance mechanisms, of which we centrally introduce five, underexplored policy recommendations. These include developing canary tokens to detect unauthorized use for producers; (automated) data filtering to remove malicious content for pre-training and post-training datasets; mandatory dataset reporting requirements for developers and vendors; improved security for datasets and data generation algorithms; and know-your-customer requirements for vendors. By considering data not just as a source of potential harm, but as a critical governance lever, this work aims to equip policymakers with a new tool for the governance and regulation of frontier AI models.

Related papers

• Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation [0.0]
  In specialized domains such as healthcare, data acquisition faces significant constraints due to privacy regulations, ethical considerations, and limited availability. The emergence of large-scale prompt-based models presents new opportunities for synthetic data generation without direct access to protected data. We review recent developments in automatic prompt optimization, following PRISMA guidelines.
  arXiv  Detail & Related papers  (2025-02-05T11:13:03Z)
• 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)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources [5.898893619901382]
  We propose a framework for the collaborative and private generation of synthetic data from distributed data holders. We replace the trusted aggregator with secure multi-party computation protocols and output privacy via differential privacy (DP) We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate algorithms MWEM+PGM and AIM.
  arXiv  Detail & Related papers  (2024-02-13T17:26:32Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
  There have been severe concerns over the trustworthiness of AI technologies. Machine and deep learning algorithms depend heavily on the data used during their development. We propose a framework to evaluate the datasets through a responsible rubric.
  arXiv  Detail & Related papers  (2023-10-24T14:01:53Z)
• Secure Multiparty Computation for Synthetic Data Generation from Distributed Data [7.370727048591523]
  Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Existing approaches assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. We propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation.
  arXiv  Detail & Related papers  (2022-10-13T20:09:17Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective [10.901568085406753]
  Article is dedicated to surveying on the state-of-the-art privacy techniques, which can be employed in Federated learning. Recent research has demonstrated that retaining data and on computation in FL is not enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system, which can be exploited in some privacy attacks.
  arXiv  Detail & Related papers  (2020-11-10T21:41:25Z)
• ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning [10.190911271176201]
  Machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. There is an immediate need for a tool that can quantify the privacy risk to data from models. We present ML Privacy Meter, a tool that can quantify the privacy risk to data from models through state of the art membership inference attack techniques.
  arXiv  Detail & Related papers  (2020-07-18T06:21:35Z)

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.