Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms

Unknown author

83 citations0/0 files storedProceedings of the AAAI/ACM Conference on AI Ethics and Society

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Ethics and Social Impacts of AI Digital Economy and Work Transformation Names, Identity, and Discrimination Research

Abstract

Ridehailing applications that collect mobility data from individuals to inform smart city planning predict each trip's fare pricing with automated algorithms that rely on artificial intelligence (AI). This type of AI algorithm, namely a price discrimination algorithm, is widely used in the industry's black box systems for dynamic individualized pricing. Lacking transparency, studying such AI systems for fairness and disparate impact has not been possible without access to data used in generating the outcomes of price discrimination algorithms. Recently, in an effort to enhance transparency in city planning, the city of Chicago regulation mandated that transportation providers publish anonymized data on ridehailing. As a result, we present the first large-scale measurement of the disparate impact of price discrimination algorithms used by ridehailing applications.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 83 citations exist upstream.

References

1998Measuring individual differences in implicit cognition: The implicit association test.2014Big Data's Disparate Impact 2015Certifying and Removing Disparate Impact 2020Mitigating bias in algorithmic hiring 2015Automated Experiments on Ad Privacy Settings 2013Discrimination in Online Ad Delivery 1983A random effects model for effect sizes.2018Detecting racial bias in algorithms and machine learning

Cited by

2023A Review of Bias and Fairness in Artificial Intelligence.2023Artificial Intelligence and Pricing 2022Platform-centric vs. multi-party service failure: an examination of consumers’ negative word of mouth about sharing economy platforms 2022American == White in Multimodal Language-and-Image AI 2023A consumer perspective of AI certification – the current certification landscape, consumer approval and directions for future research 2024AI Ethics

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.