Future and AI-Ready Data Strategies: Response to DOC RFI on AI and Open Government Data Assets
- URL: http://arxiv.org/abs/2408.01457v2
- Date: Sat, 07 Dec 2024 08:25:05 GMT
- Title: Future and AI-Ready Data Strategies: Response to DOC RFI on AI and Open Government Data Assets
- Authors: Hamidah Oderinwale, Shayne Longpre,
- Abstract summary: The following is a response to the US Department of Commerce's Request for Information (RFI) regarding AI and Open Government Data Assets.
We commend the Department for its initiative in seeking public insights on the organization and sharing of data.
In our response, we outline best practices and key considerations for AI and the Department of Commerce's Open Government Data Assets.
- Score: 6.659894897434807
- License:
- Abstract: The following is a response to the US Department of Commerce's Request for Information (RFI) regarding AI and Open Government Data Assets. First, we commend the Department for its initiative in seeking public insights on the organization and sharing of data. To facilitate scientific discovery and advance AI development, it is crucial for all data producers, including the Department of Commerce and other governmental entities, to prioritize the quality of their data corpora. Ensuring data is accessible, scalable, and secure is essential for harnessing its full potential. In our response, we outline best practices and key considerations for AI and the Department of Commerce's Open Government Data Assets.
Related papers
- On the development of open geographical data infrastructures in Latin America: progress and challenges [0.08246494848934446]
Open Geographical Data Infrastructures (OGDIs) allow citizens to access and scrutinize government and public data.
This paper analyses progress in developing OGDIs in Latin America, technological gaps, and open geographical data initiatives.
arXiv Detail & Related papers (2025-01-22T21:37:40Z) - Principles for Open Data Curation: A Case Study with the New York City 311 Service Request Data [2.3464946883680864]
The City of New York (NYC) has been at the forefront of this movement since the enactment of the Open Data Law in 2012.
The portal currently hosts 2,700 datasets, serving as a crucial resource for research across various domains.
The effective use of open data relies heavily on data quality and usability, challenges that remain insufficiently addressed in the literature.
arXiv Detail & Related papers (2025-01-14T12:06:20Z) - Methods to Assess the UK Government's Current Role as a Data Provider for AI [2.9712266483979346]
This paper serves as a technical report, explaining in-depth the designs, mechanics, and limitations of the above experiments.
It is accompanied by a complementary non-technical report on the ODI website in which we summarise the experiments and key findings, interpret them, and build a set of actionable recommendations for the UK government to take forward as it seeks to design AI policy.
arXiv Detail & Related papers (2024-11-27T19:53:05Z) - A Survey on Data Markets [73.07800441775814]
Growing trend of trading data for greater welfare has led to the emergence of data markets.
A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place.
It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data, interact.
arXiv Detail & Related papers (2024-11-09T15:09:24Z) - 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) - Data-centric AI: Perspectives and Challenges [51.70828802140165]
Data-centric AI (DCAI) advocates a fundamental shift from model advancements to ensuring data quality and reliability.
We bring together three general missions: training data development, inference data development, and data maintenance.
arXiv Detail & Related papers (2023-01-12T05:28:59Z) - Trusted Data Forever: Is AI the Answer? [5.138012450471438]
Recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records.
This paper presents preliminary results of the InterPARES Trust AI (I Trust AI) international research partnership.
arXiv Detail & Related papers (2022-02-16T11:56:41Z) - Requirements for Open Political Information: Transparency Beyond Open
Data [48.7576911714538]
We conduct user interviews to identify wants and needs among stakeholders.
We use this information to sketch out the foundational requirements for a functional political information technical system.
arXiv Detail & Related papers (2021-12-06T15:42:03Z) - Explainable Patterns: Going from Findings to Insights to Support Data
Analytics Democratization [60.18814584837969]
We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings.
ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
arXiv Detail & Related papers (2021-01-19T16:13:44Z)
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.