How to Cite:
D. Jenifar, "Human‐AI Collaboration in High‐Stakes Decision Making: Trust Calibration through Transparency" American International Journal of Computer Science and Technology, Vol. 1, No. 1, pp. 14-22, 2025.

Abstract:
As artificial intelligence (AI) systems are increasingly integrated into high-stakes decision-making domains, the challenge of fostering appropriate human trust in these systems becomes critical. Miscalibrated trust—either overtrust or distrust—can lead to significant consequences, including poor outcomes, ethical violations, or system rejection. This paper investigates how transparency mechanisms in AI systems can support effective trust calibration in human–AI collaboration. We review the current literature on trust in AI, identify key transparency dimensions (e.g., algorithmic explainability, performance metrics, uncertainty reporting), and examine how these factors influence human decision-making behavior. Through case studies in domains such as healthcare diagnostics and autonomous weapons systems, we highlight both the opportunities and limitations of transparency as a tool for trust calibration. Finally, we propose a framework for designing AI systems that foster appropriate levels of trust by aligning transparency with user needs, context specificity, and ethical imperatives.

Keywords: Human–AI Collaboration, Trust Calibration, Transparency, Explainability, High-Stakes Decision-Making, Human Factors, Responsible AI, Trustworthy AI, Algorithmic Accountability, Human–Machine Teaming.

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