iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.48550/arXiv.2407.20522
[2407.20522] Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Human-Computer Interaction

arXiv:2407.20522 (cs)
[Submitted on 30 Jul 2024]

Title:Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago

Authors:Yuhan Liu, Yuhan Zheng, Siyuan Zhang, Lydia T. Liu
View PDF HTML (experimental)
Abstract:This study examines fairness within the rideshare industry, focusing on both drivers' wages and riders' trip fares. Through quantitative analysis, we found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, health insurance status, tenure to the platform, and working hours. Despite platforms' policies not intentionally embedding biases, disparities persist based on these characteristics. For ride fares, we propose a method to audit the pricing policy of a proprietary algorithm by replicating it; we conduct a hypothesis test to determine if the predicted rideshare fare is greater than the taxi fare, taking into account the approximation error in the replicated model. Challenges in accessing data and transparency hinder our ability to isolate discrimination from other factors, underscoring the need for collaboration with rideshare platforms and drivers to enhance fairness in algorithmic wage determination and pricing.
Comments: Accepted to the Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact, co-located with the International Conference on Machine Learning, Vienna, Austria
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2407.20522 [cs.HC]
  (or arXiv:2407.20522v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2407.20522

Submission history

From: Yuhan Liu [view email]
[v1] Tue, 30 Jul 2024 03:36:55 UTC (1,018 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.HC
< prev   |   next >
new | recent | 2024-07
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)