E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.
|Original language||American English|
|Title of host publication||CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - 19 Oct 2020|
|Event||29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland|
Duration: 19 Oct 2020 → 23 Oct 2020
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||29th ACM International Conference on Information and Knowledge Management, CIKM 2020|
|Period||19/10/20 → 23/10/20|
Bibliographical noteFunding Information:
As mentioned, recent studies show that AI models that aid in judicial and management decision making can unwittingly inherit the biases of the humans making those decisions. Studying the biases in ODR and whether or not they manifest in this model would be instrumental to improving the decision making process and making it more fair. Acknowledgments: The authors would like to thank the reviewers for their insightful comments. This work was supported by US National Science Foundation, US-Israel Binational Science Foundation (NSF-BSF) grant 2017741 (Shahaf).
© 2020 ACM.
- dispute resolution
- online transactions