Track description
n°7

Artificial Intelligence (AI) systems rapidly reshape industries, organizational structures, and decision-making processes. Despite their transformative potential, the pathway toward responsible, ethical, and trustworthy AI adoption remains fraught with assumptions, blind spots, and systemic risks. Recent policy initiatives such as the European Union’s proposed AI Act highlight the critical importance of responsibly managing AI applications in the workplace, underscoring the growing global urgency around mitigating AI’s adverse effects on employee autonomy, fairness, transparency in decision-making, and inclusivity. Moreover, the widespread diffusion of Responsible AI guidelines and ethical principles demonstrates the commitment to proactively address and prevent unintended ethical consequences of AI deployments (Jobin et al., 2019). Numerous high-level frameworks – ranging from policy guidelines issued by governments to industry-driven codes of conduct – have attempted to define what “responsible” or “trustworthy” AI means in practice (Klenk, 2024; Dwivedi & Kshetri, 2023). Despite this normative clarity, both researchers and practitioners often struggle to operationalize these demands in real-world scenarios (Mittelstadt, 2019; Resseguier & Rodrigues, 2020).

In line with the ECIS 2026 conference theme, “Re-imagining Digital Technology for Business, Management, and Society,” and the International Labour Organization’s International Labour Standards, this track explicitly calls for a critical and rigorous examination of AI systems within organizational contexts. We invite submissions that offer interdisciplinary and critical perspectives to interrogate the development, deployment, and governance of ethical and responsible AI technologies in the workplace. In particular, we are interested in contributions that engage in “Red teaming” practices. Red teaming involves structured adversarial testing and stress-testing systems and policies to uncover vulnerabilities, ethical misalignments, and potential unintended consequences before large-scale deployment.

By taking a critical stance, we 1) aim to foster scholarly work that identifies and addresses organizational pain points in implementing and adopting responsible AI technologies, such as resistance to adoption among diverse groups of users, ethical conflicts, accountability gaps, or failures in aligning AI principles with day-to-day practices, and 2) welcome research that offers theoretically grounded and empirically supported insights for addressing these issues.

Submissions should address how adversarial, reflective approaches can ensure fair, equitable, and responsible integration of AI within organizational practices, safeguarding organizational efficiency and employee well-being.

• Conceptualizing responsible development, implementation, and utilization of AI for employees, groups, and organizations
• Red Teaming the assumptions embedded in AI plans, datasets, models, and adoption policies and practices
• Revealing employee dilemmas connected with recognizing/using AI at/for work, as well as the potential workarounds they can adopt
• Demystifying the potential and perils of AI for diversity and inclusion, including the potential for discrimination, bias, or systemic inequalities in organizations
• Developing quantitative and qualitative approaches to understanding, measuring, and managing the impact of implementing or using AI on individuals, organizations, and society
• Identifying and managing the potential “dark-sides” of AI, including issues related to privacy, surveillance, and employee monitoring
• Examining spatial, temporal, and behavioral work boundaries affected by AI
• Comparing human versus algorithmic decision-making and management
• Exploring existing and imagining the future “new” ways of working that are facilitated by AI, such as gig work
• Identifying how AI reorganizes professions, job categories, organizational roles, processes, and required competencies
• Investigating trust and accountability in human-AI interactions in organizational settings
• Managing human-computer interaction in the workplace
• Studying cases on (ir)responsible uses of AI at/for work in various organizations (e.g., SMEs or multinationals) and sectors (e.g., healthcare, public, or private sector companies)
• Developing and evaluating new and existing theories, models, methodologies, and frameworks for studying and evaluating AI in organizations on individuals, organizations, and society
• Investigating how AI reshapes leaders’ roles in managing diverse teams

All track chairs serve on the Editorial Board of the European Management Journal (EMJ); AT and WvZ are Associate Editors, and KK is an Editorial Board Member. Consequently, the strongest contributions to this track will be invited for submission to EMJ.