Bibliographic record
Abstract
Abstract Fair lending’s disparate impact doctrine aims to address lending disparities. But which disparities? Traditional fair lending has narrowly focused on equal outcomes—examining differences in loan approval rates or interest rates. However, this singular focus overlooks other dimensions of disparities that are essential for fair credit access. This article challenges the conventional emphasis on equal outcomes, demonstrating how it has failed to address deep-rooted inequalities in traditional credit allocation while also stifling innovation in machine-learning and alternative data. We argue that disparities in the validity of creditworthiness predictions—the accuracy with which a model identifies creditworthy applicants—importantly impact equal access to credit and, in particular, the extension of credit to the creditworthy. Despite mounting empirical evidence of the harm of validity disparities, traditional fair lending enforcement inadequately recognizes this disparity dimension, a gap that may become increasingly harmful as lending decisions rely on advanced statistical methods. Future regulatory guidance, enforcement, and supervision should explicitly recognize validity inequalities across protected groups while addressing the accompanying challenges of this more comprehensive perspective on disparities, which is essential for equitable credit allocation.
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 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 | 0.059 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".