Recommendations, Decisions, Feedback Loops, and Maybe Saving the Planet

This is a talk I gave at the CCC Workshop on Economics and Fairness.

Resources

My Papers

Papers Cited

  • Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 405–414. DOI 10.1145/3209978.3210063.
  • Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow. 2019. Fairness in Recommendation Ranking through Pairwise Comparisons. arXiv:1903.00780 [cs, stat] (March 2019).
  • Mustafa Bilgic and Raymond J Mooney. 2005. Explaining Recommendations: Satisfaction vs. Promotion. In Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research, 8.
  • Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 224–232. DOI 10.1145/3240323.3240370.
  • Dan Cosley, Shyong K Lam, Istvan Albert, Joseph A Konstan, and John Riedl. 2003. Is seeing believing?: how recommender system interfaces affect users’ opinions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 585–592. DOI 10.1145/642611.642713
  • Samuel Eilon. 1969. What Is a Decision? Management Science 16, 4 (1969), B172–B189.
  • Benjamin Fish, Ashkan Bashardoust, danah boyd, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019. Gaps in Information Access in Social Networks. In Proceedings of the World Wide Web Conference, 480–490. DOI 3308558.3313680.
  • Daniel M Fleder and Kartik Hosanagar. 2009. Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science 55, 5 (May 2009), 697–712. DOI 10.1287/mnsc.1080.0974.
  • Ben Green and Yiling Chen. 2019. Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments. In Proc. FAT* ’19, 90–99. DOI 10.1145/3287560.3287563.
  • Jonathan Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proc. CSCW '00, 241–250. DOI 10.1145/358916.358995.
  • Kartik Hosanagar, Daniel Fleder, Dokyun Lee, and Andreas Buja. 2013. Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation. Management Science 60, 4 (November 2013), 805–823. DOI 10.1287/mnsc.2013.1808.
  • Jevan Hutson, Jessie Taft, Solon Barocas, and Karen Levy. 2018. Debiasing Desire: Addressing Bias and Discrimination on Intimate Platforms. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (September 2018), 18. DOI 10.1145/3274342.
  • Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es. 2018. Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Inf. Commun. Soc. (March 2018), 1–19. DOI 10.1080/1369118X.2018.1444076.
  • Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proc. WWW ’14, 677–686. DOI 10.1145/2566486.2568012.
  • Piotr Sapiezynski, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson. 2019. Quantifying the Impact of User Attention on Fair Group Representation in Ranked Lists. In Companion Proceedings of The 2019 World Wide Web Conference - WWW ’19, 553–562. DOI 10.1145/3308560.3317595.
  • Nick Seaver. 2018. Captivating algorithms: Recommender systems as traps. Journal of Material Culture (December 2018). DOI 10.1177/1359183518820366.
  • Sylvain Senecal and Jacques Nantel. 2004. The influence of online product recommendations on consumers’ online choices. Journal of Retailing 80, 2 (2004), 159–169. DOI 10.1016/j.jretai.2004.04.001.
  • Robyn Speer. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.
  • Alain Starke, Martijn Willemsen, and Chris Snijders. 2017. “Effective User Interface Designs to Increase Energy-Efficient Behavior in a Rasch-Based Energy Recommender System.” In Proc. RecSys ’17, 65–73. DOI 10.1145/3109859.3109902.
  • Sabina Tomkins, Steven Isley, Ben London, and Lise Getoor. 2018. Sustainability at Scale: Towards Bridging the Intention-Behavior Gap with Sustainable Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems, 214–218. DOI 10.1145/3240323.3240411.
  • Martijn C Willemsen, Mark P Graus, and Bart P Knijnenburg. 2016. Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Modeling and User-adapted Interaction 26, 4 (October 2016), 347–389. DOI 10.1007/s11257-016-9178-6.
  • Michael Yeomans, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. 2019. Making Sense of Recommendations. Journal of Behavioral Decision Making (February 2019), 1–12. DOI 10.1002/bdm.2118.