Bibliographic record
Abstract
Multi-sided digital platforms, such as e-commerce, ride-sharing, and social media, face increasing regulatory and societal pressure to ensure growth strategies uphold fairness and trust. This paper proposes a novel fairness-driven growth optimization framework that integrates algorithmic fairness constraints into AI-driven models for user acquisition, retention, and engagement. Leveraging machine learning techniques, including fairness-aware contextual bandits and NSGA-II multi-objective optimization, the framework balances key performance indicators (KPIs) with equitable opportunity distribution across diverse user segments, particularly underrepresented groups. An explainable decision pipeline, using SHAP values and counterfactual fairness, ensures transparency and compliance with regulations like the EU Digital Markets Act. Extensive experiments on synthetic and real-world e-commerce datasets demonstrate a 12% reduction in systemic bias with only a 3% KPI drop. This scalable, regulation-ready solution enhances stakeholder trust and monetization potential, offering a robust framework for sustainable platform expansion in dynamic, multi-sided ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".