Frontiers: Can an Artificial Intelligence Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb
Bibliographic record
Abstract
We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasinatural experiment. Among those who adopted the algorithm, the average nightly rate decreased by 5.7%, but average daily revenue increased by 8.6%. Before Airbnb introduced the algorithm, White hosts earned $12.16 more in daily revenue than Black hosts, controlling for observed characteristics of the hosts, properties, and locations. Conditional on its adoption, the revenue gap between White and Black hosts decreased by 71.3%. However, Black hosts were significantly less likely than White hosts to adopt the algorithm, so at the population level, the revenue gap increased after the introduction of the algorithm. We show that the algorithm’s price recommendations are not affected by the host’s race—but we argue that the algorithm’s race blindness may lead to pricing that is suboptimal and more so for Black hosts than for White hosts. We also show that the algorithm’s effectiveness at mitigating the Airbnb revenue gap is limited by the low rate of algorithm adoption among Black hosts. We offer recommendations with which policy makers and Airbnb may advance smart-pricing algorithms in mitigating racial economic disparities.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".