Oct 2026· Proceedings of the AAAI/ACM Conference on AI Ethics and Society· Vol 9, pp. 2933-2934
Ethics and Social Impacts of AI
Abstract
The ethics of computing power distribution is commonly treated as a question of price, infrastructure, environmental burden, security, or geopolitical inequality. The existing frameworks illuminate important aspects of computing power, with usually translating it into another object, such as a commodity, environmental externality, security lever, or strategic resource. They struggle to explain why computing power allocation may be ethically significant before any identifiable model failure or direct harm occurs. So I claim that we need a distinct computing power ethics. First, I point out what has been overlooked in the current distribution system of computing power. Second, through cases involving public-health modelling and marginalised-language communities, I show that the Market-Permission Principle has missed out some significant normative issues. Finally, I propose we need a condition-oriented ethical theory of computing power which concerns meaningful participation in AI-mediated social life itself.
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.
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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
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
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Microsoft Research Blog· microsoft.comOct 22, 2026
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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