A Method to Assess and Explain Disparate Impact in Online Retailing
Pages 3670 - 3679
Abstract
This paper presents a method for assessing whether algorithmic decision making induces disparate impact in online retailing. The proposed method specifies a statistical design, a sampling algorithm, and a technological setup for data collection through web crawling. The statistical design reduces the dimensionality of the problem and ensures that the data collected are representative, variation-rich, and suitable for the investigation of the causes behind any observed disparities. Implementations of the method can collect data on algorithmic decisions, such as price, recommendations, and delivery fees that can be matched to website visitor demographic data from established sources such as censuses and large scale surveys. The combined data can be used to investigate the presence and causes of disparate impact, potentially helping online retailers audit their algorithms without collecting or holding the demographic data of their users. The proposed method is illustrated in the context of the automated pricing decisions of a leading retailer in the United States. A custom-built platform implemented the method to collect data for nearly 20,000 different grocery products at more than 3,000 randomly-selected zip codes. The data collected indicates that prices are higher for locations with high proportions of minority households. Although these price disparities can be partly attributed to algorithmic biases, they are mainly explained by local factors and therefore can be regarded as business necessities.
References
[1]
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2021. What We Can’t Measure, We Can’t Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 249–260.
[2]
Joshua Asplund, Motahhare Eslami, Hari Sundaram, Christian Sandvig, and Karrie Karahalios. 2020. Auditing Race and Gender Discrimination in Online Housing Markets. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 24–35.
[3]
Jack Bandy. 2021. Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–34.
[4]
Rafael Becerril-Arreola, Randolph E. Bucklin, and Raphael Thomadsen. 2021. Effects of Income Distribution Changes on Assortment Size in the Mainstream Grocery Channel. Management Science (2021).
[5]
Tomasz Bujlow, Valentín Carela-Español, Josep Sole-Pareta, and Pere Barlet-Ros. 2017. A Survey on Web Tracking: Mechanisms, Implications, and Defenses. Proceedings of the IEEE 105, 8 (2017), 1476–1510.
[6]
Alberto Cavallo. 2018. More Amazon Effects: Online Competition and Pricing Behaviors. In Jackson Hole Economic Symposium Conference Proceedings. Federal Reserve Bank of Kansas City.
[7]
Elisa Celis, Anay Mehrotra, and Nisheeth Vishnoi. 2019. Toward Controlling Discrimination in Online Ad Auctions. In International Conference on Machine Learning. PMLR, 4456–4465.
[8]
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, and Krishna P Gummadi. 2021. When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-Commerce Marketplaces. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 873–884.
[9]
Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015. Automated Experiments on Ad Privacy Settings. Proceedings on Privacy Enhancing Technologies 2015, 1 (2015), 92–112.
[10]
Elizabeth Eisenhauer. 2001. In Poor Health: Supermarket Redlining and Urban Nutrition. GeoJournal 53, 2 (2001), 125–133.
[11]
Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All the Cool Kids, How do They Fit In¿: Popularity and Demographic Biases in Eecommender Evaluation and Effectiveness. In Conference on Fairness, Accountability and Transparency. PMLR, 172–186.
[12]
Maria Eriksson and Anna Johansson. 2017. Tracking Gendered Streams. Culture unbound. Journal of Current Cultural Research 9, 2 (2017), 163–183.
[13]
Alessandro Fabris, Stefano Messina, Gianmaria Silvello, and Gian Antonio Susto. 2022. Algorithmic Fairness Datasets: the Story so Far. arXiv preprint arXiv:2202.01711 (2022).
[14]
Barbara Fick. 1997. American Bar Association Guide to Workplace Law. Times Books.
[15]
Arline T Geronimus, John Bound, and Lisa J Neidert. 1996. On the Validity of Using Census Geocode Characteristics to Proxy Individual Socioeconomic Characteristics. Journal of the American Statistical Association 91, 434 (1996), 529–537.
[16]
Isabella Gomez Sarmiento. 2019. How Online Grocery Delivery Could Help Alleviate Food Deserts. NPR (19 Dec 2019).
[17]
Aniko Hannak, Gary Soeller, David Lazer, Alan Mislove, and Christo Wilson. 2014. Measuring Price Discrimination and Steering on E-commerce Web Sites. In Proceedings of the 2014 Internet Measurement Conference. 305–318.
[18]
Thomas Hupperich, Dennis Tatang, Nicolai Wilkop, and Thorsten Holz. 2018. An Empirical Study on Online Price Differentiation. In Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy. 76–83.
[19]
Basileal Imana, Aleksandra Korolova, and John Heidemann. 2021. Auditing for Discrimination in Algorithms Delivering Job Ads. In Proceedings of the Web Conference 2021. 3767–3778.
[20]
Costas Iordanou, Claudio Soriente, Michael Sirivianos, and Nikolaos Laoutaris. 2017. Who is Fiddling with Prices¿ Building and Deploying a Watchdog Service for E-commerce. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication. 376–389.
[21]
Jongbin Jung, Sam Corbett-Davies, Ravi Shroff, and Sharad Goel. 2018. Omitted and Included Variable Bias in Tests for Disparate Impact. https://doi.org/10.48550/ARXIV.1809.05651
[22]
Allison E Karpyn, Danielle Riser, Tara Tracy, Rui Wang, and YE Shen. 2019. The Changing Landscape of Food Deserts. UNSCN nutrition 44 (2019), 46–53.
[23]
Robert P King, Ephraim Leibtag, and Ajay S. Behl. 2004. Supermarket Characteristics and Operating Costs in Low-Income Areas. Agricultural Economic Report AER-839. U.S. Deparment of Agriculture.
[24]
Will Knight. 2017. Biased Algorithms are Everywhere, and No One Seems to Care. MIT Technology Review (2017).
[25]
Anja Lambrecht and Catherine Tucker. 2019. Algorithmic Bias¿ An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads. Management Science 65, 7 (2019), 2966–2981.
[26]
Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna Wallach, and Emine Yilmaz. 2017. Auditing Search Engines for Differential Satisfaction across Demographics. In Proceedings of the 26th International Conference on World Wide Web Companion. 626–633.
[27]
Jakub Mikians, László Gyarmati, Vijay Erramilli, and Nikolaos Laoutaris. 2012. Detecting Price and Search Discrimination on the Internet. In Proceedings of the 11th ACM Workshop on Hot Topics in Networks. 79–84.
[28]
Akshat Pandey and Aylin Caliskan. 2021. Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy’s Price Discrimination Algorithms. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 822–833.
[29]
Orestis Papakyriakopoulos, Simon Hegelich, Juan Carlos Medina Serrano, and Fabienne Marco. 2020. Bias in Word Embeddings. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 446–457.
[30]
Dimitris Paraschakis and Bengt J Nilsson. 2020. Matchmaking Under Fairness Constraints: A Speed Dating Case Study. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 43–57.
[31]
Christopher Riederer and Augustin Chaintreau. 2017. The Price of Fairness in Location Based Advertising. RecSys 2017 Workshop on Responsible Recommendation (FAT/Rec) (2017).
[32]
Judy Hanwen Shen, Lauren Fratamico, Iyad Rahwan, and Alexander M Rush. 2018. Darling or Babygirl¿ Investigating Stylistic Bias in Sentiment Analysis. KDD 2018 Workshop: Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) (2018).
[33]
Debabrata Talukdar. 2008. Cost of Being P: Retail Price and Consumer Price Search Differences across Inner-City and Suburban Neighborhoods. Journal of Consumer Research 35, 3 (2008), 457–471.
[34]
Antoine Vastel, Walter Rudametkin, Romain Rouvoy, and Xavier Blanc. 2020. FP-Crawlers: Studying the Resilience of Browser Fingerprinting to Block Crawlers. In MADWeb’20-NDSS Workshop on Measurements, Attacks, and Defenses for the Web.
[35]
Thomas Vissers, Nick Nikiforakis, Nataliia Bielova, and Wouter Joosen. 2014. Crying Wolf¿ on the Price Discrimination of Online Airline Tickets. In 7th Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs 2014).
[36]
Joel Waldfogel. 2010. Who Benefits Whom in the Neighborhood¿ Demographics and Retail Product Geography. In Agglomeration economics. University of Chicago Press, 181–209.
[37]
David Zeber, Sarah Bird, Camila Oliveira, Walter Rudametkin, Ilana Segall, Fredrik Wollsén, and Martin Lopatka. 2020. The Representativeness of Automated Web Crawls as a Surrogate for Human Browsing. In Proceedings of The Web Conference 2020. 167–178.
[38]
Yong Zheng, Tanaya Dave, Neha Mishra, and Harshit Kumar. 2018. Fairness in Reciprocal Recommendations: A Speed-Dating Study. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. 29–34.
Index Terms
- A Method to Assess and Explain Disparate Impact in Online Retailing
Recommendations
Customer demand concentration in online grocery retailing: Differences between online and physical store shopping baskets
Highlights- This study explores customer demand concentration in multichannel grocery retailing.
- We use multichannel purchase panel data covering nearly the entire Japanese market.
- Customer demand is more concentrated on popular products ...
AbstractThe long-tail effect, in which online retailing has a relative advantage in selling less popular products, has been studied extensively with e-commerce growth. However, recent research has raised questions that are more nuanced than originally ...
Recommendation agent impact on consumer online shopping
Highlights This study assesses the impact of a recommendation agent (RA) on customer shopping. The results indicate that RA affects product promotion effectiveness. Product promotion effectiveness influences consumer satisfaction with the website. ...
Online word of mouth
We investigate the impact of increased technology-enabled consumer information sharing on the viability, optimal pricing, and profits of a name-your-own-price (NYOP) intermediary. The aim of our analysis is (a) to examine the NYOP's strategy when faced ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
April 2023
4293 pages
ISBN:9781450394161
DOI:10.1145/3543507
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 April 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- University of South Carolina, Office of Research
- University of South Carolina, Darla Moore School of Business
Conference
WWW '23
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 169Total Downloads
- Downloads (Last 12 months)32
- Downloads (Last 6 weeks)3
Reflects downloads up to 17 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format