ALGORITHMIC AUTHORITY AND BIAS AMPLIFICATION IN AI-SUPPORTED PRICING

Abstract

This is the conference presentation slide deck for the paper "Algorithmic Authority and Bias Amplification in AI-Supported Pricing," prepared for the 6th International Conference on Technology and Innovation Management (ICTIM 2026), 5–7 May 2026, Multimedia University, Cyberjaya, Malaysia. Presented by Eid Mohammad Frough (Faculty of Management, Multimedia University), co-authored with Hasliza Hassan and Robert Jeyakumar Nathan (Centre for Management and Marketing Innovation, Multimedia University). The paper proposes a conceptual framework — grounded in Authority Theory and Automation Bias Theory — explaining how managers' perceptions of AI-supported pricing systems as authoritative and expert can drive overreliance on algorithmic recommendations, amplifying existing biases in pricing decisions. It argues that resource constraints typical of small and medium-sized enterprises (SMEs) strengthen this reliance, since SMEs commonly implement externally developed AI systems with limited capacity to scrutinize them, and that algorithmic transparency operates as a conditional governance mechanism that can either mitigate or reinforce bias amplification depending on how decision-makers interpret it. This deck accompanies the manuscript and presents its research gap, theoretical foundation, and six propositions on algorithmic authority, managerial overreliance, and bias amplification in AI-supported pricing.