Jun 2026· Journal for Perspectives of Economic Political and Social Integration· Vol 32, pp. 111-140· 0 citations· 38 references
TL;DR
Examination of employees’ attitudes toward key dimensions of transparency and accountability in algorithmic decision-making reveals a clear acceptance of fundamental ethical norms such as human oversight and transparency, contrasted with less decisive attitudes toward more advanced organizational practices including systematic ethical training and audit procedures.
Abstract
The article examines organizational readiness for responsible artificial intelligence by analysing employees’ attitudes toward key dimensions of transparency and accountability in algorithmic decision-making. The study was designed to explore how workers perceive human responsibility for AI-generated outcomes, the need for disclosing the use of algorithms in organizational processes, the role of ethical training, and the importance of algorithmic audit mechanisms. A quantitative survey conducted in 2025 on a sample of 325 respondents was complemented with a multiple correspondence analysis, which made it possible to identify underlying structures in the distribution of responses and to capture differences in the intensity and certainty of opinions. The findings reveal a clear acceptance of fundamental ethical norms such as human oversight and transparency, contrasted with less decisive attitudes toward more advanced organizational practices including systematic ethical training and audit procedures. These results indicate that while employees support foundational principles of responsible AI, organizations still face challenges in implementing procedural and technical mechanisms that ensure full accountability. The study offers practical insights for managers and policymakers by identifying areas where competence-building, communication and institutional frameworks require strengthening. Its originality lies in combining attitudinal data with MCA to provide a nuanced picture of how transparency and accountability are understood by employees in everyday organizational contexts, contributing to the broader debate on ethical and trustworthy AI deployment.
The study aimed to examine acceptance of, and trust in, artificial intelligence in organisational management. The study was based on a quantitative approach and conducted using a diagnostic survey. The CAWI method was used to conduct the study. The questionnaire was first verified by experts in artificial intelligence and business analysis before being made available to respondents. It included questions about respondents' experience with AI, their level of competence, the perceived benefits and threats of AI, their trust in AI-assisted decisions and their assessment of this technology's impact on organisational functioning. Trust in AI is conditional, acceptance of its use hinges on human control, transparent systems, and the ability to audit decisions. While AI is seen as conducive to the development of new management practices and innovation, its direct impact on operational efficiency remains unclear. Respondents emphasise the irreplaceable role of humans in areas requiring emotional intelligence, ethics, motivation and creativity. The conclusions indicate that effectively implementing AI in management requires parallel development of employee competencies, building trust in technology and implementing ethical and supervisory standards. These findings align with the socio-technical systems approach and the human–AI cooperation model, in which technology plays a supporting role rather than replacing the human factor.
Małgorzata Oleś-Filiks· European Conference on Knowl...· 0 citations
This study examined how artificial intelligence (AI) can enhance accountability and transparency in South African municipalities. The study responded to persistent municipal governance challenges, including weak financial oversight, fragmented data systems, service delivery inefficiencies and declining public trust. A systematic review design was adopted to synthesise global, African and South African evidence on AI adoption in public administration and municipal governance. The review was guided by PRISMA principles and included 43 eligible studies selected from academic, policy and institutional sources. The Technology Acceptance Model and Institutional Theory were used to interpret both user-level and institutional factors influencing AI adoption, including perceived usefulness, perceived ease of use, staff readiness, organisational resistance, regulatory pressure and governance norms. The findings show that AI can support municipal accountability through fraud detection, procurement monitoring, audit support and anomaly identification. AI can also improve transparency through citizen-service chatbots, complaint-routing systems, open data tools and integrated information platforms. However, the review found that AI adoption in South African municipalities is constrained by poor digital infrastructure, weak data interoperability, limited technical skills, financial pressure, organisational resistance and unclear ethical governance arrangements. The study concludes that AI should be treated as a governance support tool rather than a complete solution to municipal failure. Its value depends on phased implementation, data modernisation, capacity building, legal safeguards, human oversight and inclusive citizen engagement. Future research should examine municipal-level AI implementation, public trust, algorithmic accountability and the effects of AI on equitable service delivery.
S. Nokele, Khathutshelo Matshela· Journal of Cultural Analysis...· 0 citations
The article examines how professionals from diverse occupational contexts — banking, IT, academia, business analytics, and small entrepreneurship — construct trust in artificial intelligence (AI), interpret its institutional legitimacy, and articulate ethical boundaries of automation in organizational practices. The empirical basis includes six semi-structured expert interviews conducted in 2026, with a structured author-developed guide of eight thematic blocks; the qualitative material is contextualized by author's survey data (n = 448, Saint Petersburg, 2024–2026) and by all-Russian polling data from VCIOM, Levada Center, and HSE. The research design is framed as an exploratory qualitative study with quantitative validation: interviews reconstruct experts' meaning constructs, while the survey captures the prevalence of corresponding attitudes in a broader population. Transcripts were analyzed using thematic analysis in the Braun & Clarke tradition. Three profiles of expert attitudes toward AI are identified — operational, techno-critical, and entrepreneurially-adaptive; a stable normative position of “trust but verify” emerges across professional contexts; a regulatory gap is documented between strong public demand for state oversight of AI (80%) and very low awareness of existing legal norms (16%). The article contributes to the journal's methodological debate on qualitative approaches to studying AI and proposes the framework of “exploratory qualitative research with quantitative validation” as a practical alternative to strict mixed-methods designs in limited-sample research situations.
: The research investigates the impact of Artificial Intelligence (AI) on traditional accounting methods and financial analytical tools through a qualitative research method. Accounting professionals including financial analysts and AI experts shared their knowledge about AI technology by taking part in semi-structured interviews which studied its effects on automation accuracy and decision-making processes. The case studies of companies that used AI tools helped to establish their authentic applications and the obstacles they faced. The results demonstrate how AI technology helps to decrease repetitive work while it increases the fairness and precision of financial reports and enhances the useful and timely nature of decision-making processes. The process of using AI-based tools created several major obstacles which required people to develop new competencies and handle ethical issues related to data privacy and visibility. Accounting professionals recognize a major trend that moves their field towards advisory services while reducing its focus on traditional accounting methods. The study demonstrates how professionals need to continue their development and follow ethical standards to handle AI system integration in a responsible manner. The financial sector requires organizations to handle their AI system implementation process with responsible management practices which will protect fairness and responsibility while securing financial data.
Christine Bartolome· Proceedings of the 1st Inter...· 0 citations
The widespread application of artificial intelligence (AI) in corporate resource planning and public decision-making has provided impetus for improving management efficiency and creating social value. However, the complex structure and opacity of algorithms have led to a crisis of trust, posing challenges to traditional public management accountability mechanisms. Drawing on socio-technical systems theory, this paper provides a normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation. The findings suggest that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance. Although the EU AI Act proposes a preliminary form of collaborative governance, it still has shortcomings in terms of procedural justice and the feasibility of human oversight. The governance logic should shift from individual oversight to an organization-in-the-loop approach, to achieve sustainable and responsible AI governance through the construction of a procedural justice framework.
Artificial intelligence (AI) is transforming business, government, and society at a pace that exceeds the development of governance frameworks. This article presents an academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026, examining Indonesia's strategic position in the evolving global AI landscape. The presentation argued that Indonesia should become a rule shaper rather than a rule taker by developing governance frameworks that reflect national and regional priorities instead of relying solely on external regulatory models. Responsible AI requires more than ethical principles; it depends on capable institutions, effective implementation, qualified professionals, and credible regulatory mechanisms. Key challenges identified include fragmented governance, implementation gaps, and operational risks associated with large language models, including prompt injection, hallucination, and data leakage. The presentation further emphasised the importance of independent AI assurance, certification systems, and institutional readiness to support responsible AI adoption across strategic sectors. Building digital talent and governance capability was presented as the essential foundation for reducing implementation gaps and strengthening long-term competitiveness. The presentation concluded that developing national capability while adapting international best practices provides the most appropriate pathway for enabling Indonesia to contribute to the future development of AI governance.
Patrick Rudolf Dannacher Dannacher· Proceeding Jakarta Geopoliti...· 1 citation