Introduction As the implementation of AI accelerates within organizations, concerns emerge about the ethicality of AI-driven systems and tools and the ways people use them. Although previous literature proposes numerous theories regarding how to develop and use AI ethically, questions remain about how users perceive the ethicality of AI and the effects this has on their engagement with AI tools. The study aims to explore whether and how certain perceptions of AI tool attributes, including beliefs about the explainability of the AI tool (xAI) and moral intensity dimensions (i.e., magnitude of consequences and proximity), influence perceptions of AI ethicality. Additionally, the study examines the effects of AI ethicality perceptions on the likelihood to recommend AI tools and functionality trust in AI. Methods Researchers employ a randomized, controlled study design to investigate the relationships between the antecedents, perceptions of AI ethicality, and outcomes of functionality trust and behavioral intention. Results Analyses revealed that AI ethicality perceptions significantly mediated the relationship between xAI and the likelihood to recommend the AI tool, whereas functionality trust in the AI tool significantly mediated the relationship between proximity and the likelihood to recommend the AI tool. Discussion The results indicate the strength of users’ beliefs about xAI on their perceptions of AI ethicality and how users perceive AI as less capable of making decisions for human decision recipients. The perceptual nature of the study provides a theoretical contribution to the growing body of AI-related research relevant to organizations. The findings also offer a perspective to organizations implementing AI tools, signaling the need for management to consider context and ethical perceptions when implementing AI tools in the workplace.
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
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
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026