Artificial intelligence (AI) is a rapidly advancing technology that supports economic and industrial growth, with increased use promoting global data centre expansion in areas with high freshwater access, including the Great Lakes region. To function, data centres consume large amounts of freshwater and power, resulting in carbon emissions, drought, and the acceleration of climate change. This environmental pressure affects the interconnected health of the environment, non-human animals, and humans, creating a wicked problem best considered through a One Health lens. Data centres also have notable impacts on Indigenous communities in affected regions due to the view of water as sacred and culturally important. This paper will discuss the role of grassroots organizations such as the Oregon Rural Action (ORA) and how their practices can be replicated in the Great Lakes region to protect the stakeholders involved in this wicked problem, ensuring reliable data collection and adequate legislation related to the development of AI data centres. ORA promotes community-based actions to monitor freshwater supply, host assemblies, and ensure safe drinking water for their community. These, alongside other actions such as renewable energies and sustainable systems, can be integrated into agendas which further protect communities from the impacts of data centres. Although steps have been taken to mitigate this wicked problem, standardized reporting of resource use and technological advancements are needed to achieve a solution. Artificial Intelligence Statement: Both authors attest that the submitted article is original work, completed wholly by the group without the use of generative artificial intelligence tools or similar technologies in the research, drafting, or editing processes.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026