How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact

The Future of Cyber-Enabled Influence Operations: Emergent Technologies, Disinformation, and the Destruction of Democracy

Nation-states have been embracing online influence campaigns through disinformation at breakneck speeds. Countries such as China and Russia have completely revamped their military doctrine to information-first platforms [1, 2] (Mattis, Peter. (2018). China’s Three Warfares in Perspective. War on the Rocks. Special Series: Ministry of Truth. https://warontherocks.com/2018/01/chinas-three-warfares-perspective/, Cunningham, C. (2020). A Russian Federation Information Warfare Primer. Then Henry M. Jackson School of International Studies. Washington University. https://jsis.washington.edu/news/a-russian-federation-information-war fare-primer/.) to compete with the United States and the West. The Chinese principle of “Three Warfares” and Russian Hybrid Warfare have been used and tested across the spectrum of operations ranging from competition to active conflict. With the COVID19 pandemic limiting most means of face-to-face interpersonal communi-cation, many other nations have transitioned to online tools to influence audiences both domestically and abroad [3] (Strick, B. (2020). COVID-19 Disinformation: Attempted Influence in Disguise. Australian Strategic Policy Institute. International Cyber Policy Center. https://www.aspi.org.au/report/covid-19-disinformation.) to create favorable environments for their geopolitical goals and national objectives. This chapter focuses on the landscape that allows nations like China and Russia to attack democratic institutions and discourse within the United States, the strategies and tactics employed in these campaigns, and the emergent technologies that will enable these nations to gain an advantage with key populations within their spheres of influence or to create a disadvantage to their competitors within their spheres of influence. Advancements in machine learning through generative adversarial networks [4] (Creswell, A; White, T; Dumoulin, V; Arulkumaran, K; Sengupta, B; Bharath, A. (2017) Generative Adversarial Networks: An Overview. IEE-SPM. April 2017. https://arxiv.org/pdf/1710.07035.pdf.) that create deepfakes [5] (Whit-taker, L; Letheren, K; Mulcahy, R. (2021). The Rise of Deepfakes: A Conceptual J. Littell envelope symbolenvelope symbolenvelope symbol Army Cyber Institute at the West Point, United States Military Academy, West Point, NY 10996, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A.Farhadietal. (eds.), The Great Power Competition Volume 3, https://doi.org/10.1007/978-3-031-04586-8_10 197 198 J. Littell Framework and Research Agenda for Marketing. https://journals.sagepub.com/doi/ abs/10.1177/1839334921999479.) and attention-based transformers [6](https:// arxiv.org/abs/1810.04805.) (Devlin et al., 2018) that create realistic speech patterns and interaction will continue to plague online discussion and information spread, attempting to cause further partisan divisions and decline of U.S. stature on the world stage and democracy as a whole.https://digitalcommons.usmalibrary.org/aci_books/1020/thumbnail.jp

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants,etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (un-popular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may lead the RS to make increasingly biased recommendations over time. In this paper, we view the RS environment as a chain of events that are the result of interactions between users and the RS. Based on that, we propose several debiasing algorithms during this chain of events, and evaluate how these algorithms impact the predictive behavior of the RS, as well as trends in the popularity distribution of items over time. We also propose a novel blind-spot-aware matrix factorization (MF) algorithm to debias the RS. Results show that propensity matrix factorization achieved a certain level of debiasing of the RS while active learning combined with the propensity MF achieved a higher debiasing effect on recommendations

How Sensitive is Recommendation Systems' Offline Evaluation to Popularity?

Datasets used for the offline evaluation of recommender systems are collected through user interactions with an already deployed recommender system. However, such datasets can be subject to different types of biases including a system’s popularity bias. In this paper, we focus on assessing the influence of popularity on the offline evaluation of recommendation systems. Our insights from a deeper analysis based on popularity-stratified sampling reveal that the current offline evaluation of recommendation systems are sensitive to popular items, raising questions about conclusions driven from the offline comparison of recommendation models

Modeling and Counteracting Exposure Bias in Recommender Systems

What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after several iterations of machine learning predictions and user feedback. Machine-caused biases risk leading to undesirable social effects ranging from polarization to unfairness and filter bubbles. In this paper, we study the bias inherent in widely used recommendation strategies such as matrix factorization. Then we model the exposure that is borne from the interaction between the user and the recommender system and propose new debiasing strategies for these systems. Finally, we try to mitigate the recommendation system bias by engineering solutions for several state of the art recommender system models. Our results show that recommender systems are biased and depend on the prior exposure of the user. We also show that the studied bias iteratively decreases diversity in the output recommendations. Our debiasing method demonstrates the need for alternative recommendation strategies that take into account the exposure process in order to reduce bias. Our research findings show the importance of understanding the nature of and dealing with bias in machine learning models such as recommender systems that interact directly with humans, and are thus causing an increasing influence on human discovery and decision makingComment: 9 figures and one table. The paper has 5 page

How Sensitive is Recommendation Systems' Offline Evaluation to Popularity?

Datasets used for the offline evaluation of recommender systems are collected through user interactions with an already deployed recommender system. However, such datasets can be subject to different types of biases including a system’s popularity bias. In this paper, we focus on assessing the influence of popularity on the offline evaluation of recommendation systems. Our insights from a deeper analysis based on popularity-stratified sampling reveal that the current offline evaluation of recommendation systems are sensitive to popular items, raising questions about conclusions driven from the offline comparison of recommendation models