Sustainable Data Democratization: A Multifaceted Investment for an Equitable Future
- URL: http://arxiv.org/abs/2408.14627v1
- Date: Mon, 26 Aug 2024 20:45:48 GMT
- Title: Sustainable Data Democratization: A Multifaceted Investment for an Equitable Future
- Authors: Michela Taufer, Valerio Pascucci, Christine R. Kirkpatric, Ian T. Foster,
- Abstract summary: The urgent need for data democratization in scientific research was the focal point of a panel discussion at SC23.
We advocate for strategic investments in financial, human, and technological resources for sustainable data democratization.
- Score: 10.572912037294522
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The urgent need for data democratization in scientific research was the focal point of a panel discussion at SC23 in Denver, Colorado, from November 12 to 17, 2023. This article summarizes the outcomes of that discussion and subsequent conversations. We advocate for strategic investments in financial, human, and technological resources for sustainable data democratization. Emphasizing that data is central to scientific discovery and AI deployment, we highlight barriers such as limited access, inadequate financial incentives for cross-domain collaboration, and a shortage of workforce development initiatives. Our recommendations aim to guide decision-makers in fostering an inclusive research community, breaking down research silos, and developing a skilled workforce to advance scientific discovery.
Related papers
- The Role of Computing Resources in Publishing Foundation Model Research [84.20094600030092]
We evaluate the relationship between these resources and the scientific advancement of foundation models (FM)<n>We reviewed 6517 FM papers published between 2022 to 2024, and surveyed 229 first-authors to the impact of computing resources on scientific output.<n>We find that increased computing is correlated with national funding allocations and citations, but our findings don't observe the strong correlations with research environment.
arXiv Detail & Related papers (2025-10-15T14:50:45Z) - Funding AI for Good: A Call for Meaningful Engagement [12.728614701701273]
Funding agendas play a crucial role in framing AI4SG initiatives and shaping their approaches.<n>We reveal dissonances between AI4SG's stated intentions for positive social impact and the techno-centric approaches that some funding agendas promoted.
arXiv Detail & Related papers (2025-09-15T21:04:42Z) - AI for Scientific Discovery is a Social Problem [6.165263713559601]
We argue that the primary barriers are social and institutional.<n>We highlight four interconnected challenges: community dysfunction, research priorities with misaligned upstream needs, data fragmentation, and infrastructure inequities.
arXiv Detail & Related papers (2025-09-08T11:49:52Z) - Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences [50.9036832382286]
We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
arXiv Detail & Related papers (2025-05-22T12:52:34Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)
We identify benefits, harms, and future research directions for the four main themes.
We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Enabling the Digital Democratic Revival: A Research Program for Digital
Democracy [68.02254954746476]
This white paper outlines a long-term scientific vision for the development of digital-democracy technology.
It arose from the Lorentz Center Workshop on Algorithmic Technology for Democracy'' (Leiden, October 2022)
arXiv Detail & Related papers (2024-01-30T10:12:49Z) - Data Science for Social Good [2.8621556092850065]
We present a framework for "data science for social good" (DSSG) research.
We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems.
We hope that this article and the special issue will spur future DSSG research.
arXiv Detail & Related papers (2023-11-02T15:40:20Z) - Information Forensics and Security: A quarter-century-long journey [66.16120845232525]
Information Forensics and Security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes.
For over a quarter century since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era.
arXiv Detail & Related papers (2023-09-21T15:13:35Z) - Enhancing Artificial intelligence Policies with Fusion and Forecasting:
Insights from Indian Patents Using Network Analysis [0.0]
This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies.
By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape.
arXiv Detail & Related papers (2023-04-20T18:37:11Z) - Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions.
To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter.
Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
arXiv Detail & Related papers (2023-03-18T19:17:47Z) - The State of Human-centered NLP Technology for Fact-checking [7.866556977836075]
Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, and disrupting democratic elections and financial markets.
A growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking.
Despite tremendous growth in such research, practical adoption of NLP technologies for fact-checking still remains in its infancy today.
arXiv Detail & Related papers (2023-01-08T15:13:13Z) - Coordinated Science Laboratory 70th Anniversary Symposium: The Future of
Computing [80.72844751804166]
In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary.
We summarize the major technological points, insights, and directions that speakers brought forward during the symposium.
Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
arXiv Detail & Related papers (2022-10-04T17:32:27Z) - Exploring Tenets of Data Democratization [0.0]
Data democratization is an ongoing process that broadens access to data and facilitates employees to find, access, self-analyze, and share data without additional support.
This paper explores the tenets of data democratization through an in-depth review of the literature.
The analysis identified twelve attributes that enable data democratization based on the literature review.
arXiv Detail & Related papers (2022-06-24T03:00:29Z) - Learnings from Frontier Development Lab and SpaceML -- AI Accelerators
for NASA and ESA [57.06643156253045]
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines.
We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA.
FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
arXiv Detail & Related papers (2020-11-09T21:23:03Z) - Fine-grained Financial Opinion Mining: A Survey and Research Agenda [50.27357144360525]
We first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview on the issues already tackled.
We propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore.
arXiv Detail & Related papers (2020-05-05T01:07:07Z)
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.