On the Desiderata for Online Altruism
Bibliographic record
Abstract
Online donation platforms help equalize access to opportunity and funding in cases where inequalities exist. In the context of public school education in the United States, for instance, financial inequalities have been shown to be reflected in the educational system, since schools are primarily funded through local property taxes. In response, private charitable donation platforms such as DonorsChoose.org have emerged seeking to alleviate systemic inequalities. Yet, the question remains of how effective these platforms are in redressing existing funding inequalities across school districts. Our analysis of donation data from DonorsChoose shows that such platforms may in fact be ineffective in mitigating existing inequalities or may even exacerbate them. In this paper, we explore how online educational charities could direct more funding towards more impoverished schools without compromising their donors' freedom of choice with respect to donation targets. Seeking to answer this question, we draw on the line of work on choice architectures in behavioral economics and pose a novel research question on the impact of interface design on equity in socio-technical systems. Through controlled experiments, we demonstrate how simple interface design interventions - such as modifying default rankings or displaying additional information about schools - might lead to changes in donation distributions helping platforms direct more funding towards schools in need. Going beyond online educational charities, we hope that our work will bring attention to the role of interface design nudges in the social requirements of online altruism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".