Sep 2026· Leadership & Organization Development Journal· 0 citations· 45 references
Ethics and Social Impacts of AI
TL;DR
This study develops a comprehensive understanding of AI leadership by examining how traditional leadership theories and psychological constructs explain effective leadership behaviors in AI-intensive organizational contexts by synthesizing theories from leadership studies, cognitive psychology, organizational behavior, and AI ethics to develop an integrated AI-adaptive leadership framework.
Abstract
This study develops a comprehensive understanding of AI leadership by examining how traditional leadership theories and psychological constructs explain effective leadership behaviors in AI-intensive organizational contexts.
This study employs a systematic conceptual review, synthesizing theories from leadership studies, cognitive psychology, organizational behavior, and AI ethics to develop an integrated AI-adaptive leadership framework (Jaakkola, 2020; Torraco, 2016). Peer-reviewed literature was identified, screened and thematically synthesized across transformational, servant and ethical leadership, mapped against growth mindset, antifragility and consideration of future consequences.
AI leadership is inherently multidimensional, emerging from the integration of transformational, servant, and ethical leadership with growth mindset, antifragility and consideration of future consequences. Thematic synthesis identified four clusters linking these frameworks to core leadership capacities: digital innovation, responsible AI governance, adaptive resilience and long-term strategic foresight. These clusters converge into a multi-theoretical model bridging management and psychology, offering a framework for navigating human-AI collaboration and organizational transformation.
The study guides organizations in cultivating AI-ready leaders by emphasizing ethical, adaptive and human-centered competencies alongside technological foresight and learning-oriented cultures.
This research integrates leadership and psychological theories to offer a multidimensional perspective on AI leadership, bridging theoretical and practical insights not addressed in previous studies.
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
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.