Work

Towards Fair Representation in AI-Mediated Decision-Making: A Conceptual Model for Socio-Technical Contexts

Year: 2026

Type: conference-paper

Abstract: AI-mediated decision-making enhances human performance and efficiency in small and medium-sized enterprises (SMEs). However, compared with human decision-making, it raises concerns about human-centred principles such as transparency, fairness, and representation. In particular, formal worker representatives, such as works councils, who have relied on conventional oversight, often lack the technical entry points required to influence, oversee, or negotiate black-box AI-mediated decisions. This mismatch creates a representation gap that challenges the legitimacy of algorithmic outcomes and raises doubts about whether workers’ voices are adequately considered. Consequently, despite ongoing efforts to balance biased AI, it remains unclear how fair representation can be conceptualized and practically realized within digitally mediated decision-making. Building on this foundational challenge, the study asks how workers’ voices can shape AI-mediated decisions concerning the shop floor; how fair representation can be conceptually defined and embedded as a human-centred principle. To achieve this, the study first evaluates existing socio-technical and institutional attempts to embed human-centred principles into algorithmic decision-making, from social forms of representation, such as collective bargaining, to technical forms, such as Multi-Agent Systems (MAS). Secondly, it addresses the limitations of these existing approaches and comprehensively bridges the representation gap by developing a novel five-layered model, grounded in a case study and shop-floor insights. The model spans Level 0 (problem recognition) to Level 4 (AI-mediated decisions via MAS), integrating worker interests into AI-driven processes to reduce centralized, unilateral decision-making and ensure a substantive role for workers and their representatives. The study shows that bridging the representation gap requires more than algorithmic systems; the proposed framework highlights a deeper, trust-building form of worker involvement at the shop-floor level. Since AI is limited in fulfilling the social and legitimate representational functions of works councils, their participation at the proposed layers becomes essential. This interdependence forms a “nested representation”, integrating workers’ needs into AI tools to strengthen the human-centred foundations of AI-supported decisions in SMEs.

Source: AHFE international

DOI: 10.54941/ahfe1007364

Authors: Noushin Qeybi, Khadija Sabiri, Gustavo Vieira

Institutions: RWTH Aachen University, MORE – Laboratório Colaborativo Montanhas de Investigação – Associação, Universidade Politécnica de Bragança, Research Centre in Digitalization and Intelligent Robotics

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funders: European Commission, HORIZON EUROPE Framework Programme

Award: Flexibilization of complex Ecosystems using Democratic AI based Decision Support and Recommendation Systems at Work