Costs and Benefits
AI and Governance: A Comparative Analysis of Civic Participation, Social Equity, and Policy-Making in Developed and Developing Nations
Paper Presentation in a Themed Session Leopoldino Jeronimo
This research scrutinizes the transformative role of Artificial Intelligence (AI) in governance by conducting a comparative analysis across both developed and developing nations. By examining AI’s impact on civic participation, social equity, and public policy in the United States, Germany, Japan, India, Brazil, and Kenya, this study seeks to loosen the complexities of AI implementation in diverse political and economic contexts. These nations were chosen for their contrasting governance structures and levels of AI adoption, providing a comprehensive view of how AI is reshaping governance on a global scale. The research employs a mixed-methods approach integrating secondary data analysis, literature review, and meta-analysis of existing case studies. This methodology allows the exploration of existing data that illustrates how AI influences governance dynamics, from enhancing democratic processes in established democracies to addressing equity challenges in emerging economies. Furthermore, the study extends its scope to global organizations like the United Nations, analyzing how AI policies at the international level influence national practices. The goal is to develop ethical frameworks that ensure AI aligns with principles of justice and equity, offering policy recommendations tailored to different political systems. Ultimately, this research contributes to the broader discourse on AI’s potential to support the Sustainable Development Goals (SDGs) and foster more inclusive and equitable governance worldwide.
Blending Technology, Interdisciplinary Collaboration, and Advocacy: A Critical Recipe for Fostering a Sustainable and Strong Future
Paper Presentation in a Themed Session Mioara Diaconu, Laura Racovita
Technology, including artificial intelligence (AI), could play a pivotal role in addressing interdisciplinary and interprofessional connections between humans and the natural environment around them as communities around the world are facing pressing challenges such as climate change, social inequities, and public health crises. Innovating technologies could offer tools for managing complex systems, predicting outcomes, and enabling cross-sector collaboration in real-time. AI could facilitate data-driven decision-making, which enhances the ability of professionals from diverse fields - such as public health, environmental science, social work, and public policy - to collaborate effectively. This is crucial for addressing the interconnected nature of sustainability, health, and mental health. For instance, AI can support sustainable agriculture practices by predicting environmental patterns, while also enabling the development of personalized health interventions that consider both physical and mental well-being. In addition, technology can foster a global dialogue on best practices, allowing communities and various professions to share with each other knowledge and strategies in real-time. By promoting evidence-based policies that integrate technological advancements and interdisciplinary approaches, local leaders can contribute to a global framework for sustainability and health. Furthermore, by prioritizing mental health as a core component of health policy, communities can ensure that the psychological well-being of their populations is not overlooked in the pursuit of sustainability. As local decisions shape global outcomes, the integration of technology, interdisciplinary collaboration and robust advocacy will be critical in fostering a sustainable and strong future. Lessons learned and recommendations for education and practice are explored in this paper.
Reimagining Social Sciences Research Through Omni Intelligence: Beyond Interdisciplinarity in Addressing Complex Global Challenges View Digital Media
Paper Presentation in a Themed Session Tatjana Brkic
As AI reshapes the landscape of research, its potential role in transdisciplinary inquiry remains underdefined, especially in fields rooted in lived experience and value-based practice such as social entrepreneurship (SE). This paper explores AI and human collaboration as a dynamic method for identifying, synthesizing, and integrating knowledge relevant to dual value creation, both economic and social. Building on the Interdisciplinary Research Process (Repko&Szostak, 2025), the author collaborates with two generative AI systems combining lived practitioner insights, academic knowledge from three disciplines and AI logic and data. Through examples grounded in global SE practice, the paper demonstrates how AI can enhance thematic analysis, conceptual integration, and comparative synthesis, while co-reflecting on human lived experiences and contextual complexity. Notably, it highlights how AI and human interaction contributes to identifying best practices in dual value creation beyond academia by drawing connections across diverse academic and cultural approaches to SE, including jugaad (India), biomimicry (global ecological design), achaprovemento (South America), and 共享经济 (gongxiang jingji (Chinese concept of the sharing economy). These cultural insights, when combined with practitioner experience and scholarly contributions, enable a more grounded and globally relevant SE framework. This paper argues that AI, when positioned not as a replacement but as a reflective co thinker, can support deeper transdisciplinary knowledge production. It invites dialogue on the ethical and epistemological responsibilities of using AI in educational and social innovation contexts and proposes a model where machine intelligence is used to surface embedded knowledge from practice, culture, and theory to support transformative SEE worldwide.
Artificial Intelligence in Corporate Governance: A New Frontier in Agency Cost Mitigation
Paper Presentation in a Themed Session Inah Okpa
The failure of traditional corporate governance (CG) practices to adequately address the agency problems between shareholders and managers has motivated this novel academic enquiry. This paper investigates the impact of Artificial Intelligence adoption (AIA) on agency costs mitigation and examines the role of AIA in enhancing the effectiveness of traditional CG mechanisms in reducing agency cost. We utilised an international sample comprising 82,323 firm–year observations from 7,537 listed active firms in the US, UK, Canada, Germany, Australia, and China, between the period 2010 to 2023. The main findings indicate that AIA significantly mitigates agency costs. Additionally, AIA reduces information asymmetry, improves executive compensation structure, and enhances the effectiveness of board and audit quality monitoring mechanisms, leading to reduction in agency costs. These findings are robust to battery of analyses including alternative independent, dependent, and control variables, control interactions, lagged variables, change regression, difference–in–difference estimation, panel dynamic GMM and GEE model specifications, instrumental variable methods, propensity score match (PSM), with additional analysis of 1998 dead firms comprising a sample of 14,230 firm–year observations to mitigate survivorship bias. Our heterogeneity analyses further underscore important complementarities between AIA and strong CG practices in addressing agency problems within firms. Overall, this study provides novel empirical evidence that AI holds the promise of mitigating agency problems and reshaping CG practices for more effective agency outcomes, with crucial implications for research and practice in utilizing AI technologies for continuous monitoring, governance and accountability.
