Outcome Over Process: Advancing Both Fairness and Performance in Crowdfunding Through ML-Assisted Promotion

Crowdfunding platforms can influence project success through their promotion policy, which determines which projects receive enhanced visibility. Promotion policies can directly influence inclusivity on the platform and potentially address well-documented disparities across creators. This study examines how alternative machine learning (ML) problem framings for promotion policy affect fairness and business performance. Using data from 118,780 Kickstarter projects, we compare three ML framings: a process framing that learns to reproduce historical employee promotion decisions, a success framing that predicts whether projects will meet their funding goals, and a backers framing that predicts whether projects will attract a large number of backers. We evaluate the resulting counterfactual promotion sets using multiple fairness and performance measures. In our context, the success framing produces promotion sets with more favorable fairness outcomes and business performance outcomes than the other framings, possibly because it applies more consistent promotion decisions across similar projects. These findings demonstrate that alternative ML problem framings can produce meaningfully different promotion policies, even when trained on the same underlying data. While the success framing prioritizes practical projects, the backers framing favors projects with higher funding goals. Importantly, carefully designing the ML problem framing for promotion policies can align fairness and performance objectives, countering concerns that inclusivity necessarily comes at the expense of performance. This research contributes to the information systems literature by demonstrating that ML problem framing is a consequential choice in platform governance. It offers practical guidance for digital platforms seeking to improve fairness and business performance through ML-assisted promotion policies.