Algorithmic Transparency Loops: User Feedback as a Governance Lever in Crowdfunding Platforms

Wang et al. (2025) emphasize the positive impact of algorithmic transparency on trust and crowdfunding success. But what if platform governance evolved from passive transparency (just explaining algorithms) to active “transparency loops,” where user feedback directly influences algorithmic behavior? This research would design and test real-time feedback systems that allow users (investors, project creators) to flag biases, suggest ranking adjustments, or veto algorithmic decisions, feeding these inputs into ongoing algorithm retraining. The novelty lies in closing the governance loop: giving users not just visibility, but true participatory agency over platform logic. Such an approach could reveal new forms of algorithmic accountability, mitigate unforeseen biases, and create a living governance system that adapts to user expectations and emergent behaviors. If successful, it could reshape trust dynamics and set a new standard for FinTech and beyond.

References:

1. User-Centric Intelligent Recommendations for E-Commerce Crowdfunding Success. Yunfeng Wang, Junhai Wang, Ibrahim Othman (2025). Journal of Organizational and End User Computing.