Empowering Local Communities Using Artificial Intelligence
- URL: http://arxiv.org/abs/2110.02007v1
- Date: Tue, 5 Oct 2021 12:51:11 GMT
- Title: Empowering Local Communities Using Artificial Intelligence
- Authors: Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Himanshu Verma, Andrea Mauri,
Illah Nourbakhsh, Alessandro Bozzon
- Abstract summary: It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
- Score: 70.17085406202368
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many powerful Artificial Intelligence (AI) techniques have been engineered
with the goals of high performance and accuracy. Recently, AI algorithms have
been integrated into diverse and real-world applications. It has become an
important topic to explore the impact of AI on society from a people-centered
perspective. Previous works in citizen science have identified methods of using
AI to engage the public in research, such as sustaining participation,
verifying data quality, classifying and labeling objects, predicting user
interests, and explaining data patterns. These works investigated the
challenges regarding how scientists design AI systems for citizens to
participate in research projects at a large geographic scale in a generalizable
way, such as building applications for citizens globally to participate in
completing tasks. In contrast, we are interested in another area that receives
significantly less attention: how scientists co-design AI systems "with" local
communities to influence a particular geographical region, such as
community-based participatory projects. Specifically, this article discusses
the challenges of applying AI in Community Citizen Science, a framework to
create social impact through community empowerment at an intensely place-based
local scale. We provide insights in this under-explored area of focus to
connect scientific research closely to social issues and citizen needs.
Related papers
- Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - AI for social science and social science of AI: A Survey [47.5235291525383]
Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities.
The increasing human-like capabilities of AI are also attracting attention in social science research.
arXiv Detail & Related papers (2024-01-22T10:57:09Z) - Artificial Intelligence and Human Geography [1.6135760596596367]
This paper examines the recent advances and applications of AI in human geography.
It includes the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design.
arXiv Detail & Related papers (2023-12-14T11:20:22Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - AI for Science: An Emerging Agenda [30.260160661295682]
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling"
The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains.
Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
arXiv Detail & Related papers (2023-03-07T20:21:43Z) - FATE in AI: Towards Algorithmic Inclusivity and Accessibility [0.0]
To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented.
This study examines FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI.
To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design.
arXiv Detail & Related papers (2023-01-03T15:08:10Z) - GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial
Intelligence Research [4.723592249469651]
In this article, we revisit and discuss the state of GeoAI open research directions.
The workshop series has fostered nexus for geoscientists, computer scientists, engineers, entrepreneurs, and decision-makers.
arXiv Detail & Related papers (2022-10-20T18:02:17Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z)
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.