Zuzana Virglerova, Tomas Bata University in Zlín

Nicola Capolupo, San Raffaele Roma University

The aim of this special issue is to explore how algorithmic and behavioral biases influence the adoption, use, and outcomes of Artificial Intelligence (AI) in organizational settings. As AI systems increasingly shape decision-making, human resource management, operational processes, and strategic planning, understanding how biases emerge and interact within socio-technical systems has become a critical scholarly and societal challenge.

The special issue seeks to integrate interdisciplinary perspectives to explain how biases arise both from AI systems and from the human and organizational decisions that design, implement, interpret, and govern them. It offers an original contribution by bridging two research streams that are often treated separately: algorithmic biases embedded in data, models, and computational architectures, and behavioral biases rooted in human cognition, organizational routines, and institutional structures. Building on socio-technical systems theory, AI is conceived as co-constructed by technologies, individuals, and the environments.

The originality of this issue lies in examining the dynamic interplay between technical biases (e.g., biased training data, model opacity, feedback loops) and cognitive and organizational biases (e.g., overconfidence, automation bias, anchoring, resistance to change), and how these interactions shape organizational outcomes. Recent research has emphasized the role of behavioral biases in organizational and entrepreneurial decision-making, highlighting the contextual and institutional moderators of such biases. Emerging work further demonstrates how digital technologies and AI systems interact with managerial cognition and organizational structures, creating hybrid forms of bias that are neither purely human nor purely algorithmic. These insights underscore the need for integrated frameworks and methodologies that capture the socio-technical nature of AI adoption in organizations.

The topicality of this special issue is reinforced by the rapid diffusion of AI across organizational domains, including human resource management and recruitment, decision support systems, circular economy and sustainability models, consumer behavior analytics, and work process automation. Public debates on algorithmic discrimination, opacity, accountability, and the future of work have intensified, highlighting the urgent need for rigorous research on fairness, transparency, and governance in AI-enabled organizations. Furthermore, global regulatory initiatives such as the European AI Act, along with ethical frameworks proposed by international organizations, emphasize the societal relevance of understanding and mitigating bias in AI-driven processes.

Empirical qualitative and quantitative studies are welcome; however, submissions must be based on international data or multi-country contexts and should not be limited to a single national perspective. Literature reviews are welcome if they are systematic or bibliometric in nature; exploratory or narrative metasyntheses will not be accepted. Conceptual papers are also encouraged, only if offering exceptionally strong and innovative theoretical insights.

Potential topics

• Algorithmic biases in AI systems applied to HR and decision-making processes
• Cognitive and behavioral biases of users interacting with AI systems
• Governance, ethics, and regulation of AI in organizations
• Methodologies for identifying and mitigating socio-technical biases in work processes
• Organizational and social impacts of AI on equity, environment, inclusion, and job quality

Timeline

• June 1, 2026: Opening date for manuscripts submissions
• July 31, 2027: Closing date for manuscripts submission