arXiv:2606.09891v1 Announce Type: new Abstract: Ranking in digital marketplaces is a dynamic exposure-allocation mechanism: displayed items shape discovery trajectories and success events logged by the platform to update future allocation policies. Modern ranking systems rely heavily on exposure-confounded signals (e.g. popularity estimates, CTR/CVR aggregates, and ID-based representation), because they are highly predictive under stationary demand. Yet this predictive power can become a learning shortcut: early access to exposure-dependent belief signals steers optimization toward over-relian
The proliferation of advanced AI ranking systems in digital marketplaces necessitates robust solutions to address inherent biases and 'learning shortcuts' that limit long-term effectiveness.
This research offers a method to enhance the resilience and fairness of AI-driven ranking and allocation systems, which are foundational to e-commerce, content platforms, and resource distribution.
The proposed 'Representation Curriculum' introduces a stagewise training approach for AI ranking models, moving beyond exposure-confounded signals to build more robust and generalizable representations.
- · Digital marketplace operators
- · AI fairness and ethics researchers
- · Consumers seeking fairer recommendations
- · Developers of AI ranking systems
- · Opportunistic content creators
- · Platforms reliant on easy 'learning shortcuts'
- · Naive single-stage AI ranking models
Improved accuracy and fairness in exposure-sensitive AI ranking systems by de-confounding signals.
Increased user trust and satisfaction in digital platforms, potentially leading to greater engagement and economic activity.
New competitive advantages for platforms that successfully implement and scale such robust, bias-aware AI models over those that do not.
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Read at arXiv cs.LG