Algorithmic transparency and assessing effects of algorithmic ranking
Dean Eckles · 2022
1. At established platforms, algorithmic ranking and recommendation involve using many signals and are typically not aimed at simply maximizing short-run engagement.2. Quantifying the impacts of algorithmic ranking is quite difficult,even with access to proprietary data. This is not only becauseof the complexity of these technical systems, but due to people’scomplex and often strategic responses to changes in algorithms.3. We lack clear evidence about broader benefits or harms of algorithmic ranking. Nonetheless, simple rankings and recommendations (e.g., chronological, overall popularity) can make some formsof undesirable strategic behavior easier.4. Policy-makers can protect the ability of external researchers toprobe these systems, and they can provide clear paths for platforms to retain and share data in privacy-preserving ways.