THE UNINTENDED CONSEQUENCES OF ALGORITHMIC TRANSPARENCY ON TRUST: A MULTI-DISCIPLINARY ANALYSIS
DOI:
https://doi.org/10.63356/asb.2025.002Keywords:
algorithmic transparency, cognitive overload, strategic opacity, trust preservation, agent-based modelingAbstract
This paper challenges the widely held assumption that increased algorithmic transparency universally enhances user trust. Through interdisciplinary analysis spanning legal, ethical, and technical domains, we demonstrate that, paradoxically, excessive transparency can erode trust in algorithmic systems. Using agent-based simulations, we model how users with varying cognitive thresholds respond to transparency events, revealing what can be called the “transparency overload” phenomenon. Our findings suggest that strategic opacity－the deliberate limitation of certain types of algorithmic disclosure－may better preserve trust in specific contexts. We propose "context-dependent transparency" as an alternative framework that balances accountability with user' cognitive limitations. This research has significant implications for policymakers and system designers seeking to build genuinely trustworthy algorithmic systems rather than merely transparent ones.
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