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1. Testing a Recommender System for Self-Actualization

   Wilkinson, Daricia; Sivakumar, Saadhika; Sinha, Pratitee; Knijnenburg, Bart P. (January 2017, Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems)

   Traditionally, recommender systems were built with the goal of aiding users’ decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users’ preferences, these systems create an isolated universe of information for each user, which may limit their perspectives and promote complacency. In this paper, we describe our research plan to test a novel approach to recommender systems that goes beyond “good recommendations” that supports user aspirations and exploration. 

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2. Getting Help

   https://doi.org/10.59015/wlr.UWDQ2395  

   Young, Kathryne M (September 2024, Wisconsin Law Review)

   In half of U.S. households, at least one person faces a civil justice problem each year. These problems range from eviction to divorce, benefits denials to neighbor disputes, and medical debt to employment discrimination. Most will never reach a court or even a lawyer—indeed, most will never be solved at all. Unresolved civil legal problems cause financial instability, housing insecurity, and poor mental and physical health—burdens disproportionately borne by Black, Latinx, multiracial, and low-income Americans. Although we know a great deal about the existence and distribution of civil justice problems, we know much less about how to solve them. Doing so requires empirical research about help-seeking: where people go for assistance, why they pursue some resources but avoid others, and whether and how race and class shape patterns of help-seeking. We need solutions that align with everyday people’s lived experiences. This Article investigates help-seeking from the perspective of ordinary people. Its findings can better equip lawyers, justice innovators, and program designers to create novel access to justice solutions from the perspective of ordinary people. Leveraging data from a nationally representative survey, this Article analyzes over 47,000 quantitative responses and 100,000 words of open-ended answers, unearthing powerful findings about how Americans think about getting help when they face a complex, early-stage problem with legal implications: here, an eldercare scenario. People gravitate towards sources they view as experienced and private, and those which offer advice, not information—a crucial distinction in light of legal regulatory regimes. They gravitate away from sources they view as bureaucratic, uncaring, or too extreme; these perceptions hinge on source type. Identities such as political affiliation and religiosity are crucial predictors of help-seeking behavior, denoting a need for diversified outreach strategies to polarized groups. By focusing on help-seeking for early-stage problems, this Article shifts the conversation from the existence of legal needs to laying the empirical groundwork for interventions that center the perspective of ordinary Americans. Doing so will better equip us to forge tools that can stop the corrosive effects of unsolved civil legal problems. 

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3. LuckyFind: Leveraging Surprise to Improve User Satisfaction and Inspire Curiosity in a Recommender System

   https://doi.org/10.1145/3406522.3446017  

   Niu, Xi; Al-Doulat, Ahmad (March 2021, CHIIR '21: Proceedings of the 2021 ACM SIGIR Conference on Human Information Interaction and Retrieval)

   null (Ed.)

   The growing amount of online information today has increased opportunity to discover interesting and useful information. Various recommender systems have been designed to help people discover such information. No matter how accurately the recommender algorithms perform, users’ engagement with recommended results has been complained being less than ideal. In this study, we touched on two human-centered objectives for recommender systems: user satisfaction and curiosity, both of which are believed to play roles in maintaining user engagement and sustain such engagement in the long run. Specifically, we leveraged the concept of surprise and used an existing computational model of surprise to identify relevantly surprising health articles aiming at improving user satisfaction and inspiring their curiosity. We designed a user study to first test the validity of the surprise model in a health news recommender system, called LuckyFind. Then user satisfaction and curiosity were evaluated. We find that the computational surprise model helped identify surprising recommendations at little cost of user satisfaction. Users gave higher ratings on interestingness than usefulness for those surprising recommendations. Curiosity was inspired more for those individuals who have a larger capacity to experience curiosity. Over half of the users have changed their preferences after using LuckyFind, either discovering new areas, reinforcing their existing interests, or stopping following those they did not want anymore. The insights of the research will make researchers and practitioners rethink the objectives of today’s recommender systems as being more human-centered beyond algorithmic accuracy. 

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4. Phishing Attack Awareness

   Collins, Isaac; Muhammad, Jean (January 2022, ADMI 2022:The Symposium of Computing at Minority Institutions)

   Phishing Attacks, cybercrime in which a target(s) is contacted by someone posing as a legitimate institution to lure individuals into providing sensitive data. The problem at stake is most people who use smartphones, tablets, and computers do not know how to protect themselves from phishing attacks, making themselves susceptible to data theft. This paper will use research of phishing attack types, what makes those more vulnerable to phishing attacks, and how to detect and report them. Additionally, I will interview a Department of Homeland Security employee working in cybersecurity as they have an insightful perspective on the problem. I will combine my research and in-person interview to conduct a literary search on the best methods to prevent and avoid phishing attacks for the average technology user to practice, especially children. This will give a valuable solution to the problem, decreasing the rate at which phishing attacks are successful. 

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5. Toward A Two-Sided Fairness Framework in Search and Recommendation

   https://doi.org/10.1145/3576840.3578332  

   Liu, Jiqun (March 2023, CHIIR '23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval)

   As artificial intelligence (AI) assisted search and recommender systems have become ubiquitous in workplaces and everyday lives, understanding and accounting for fairness has gained increasing attention in the design and evaluation of such systems. While there is a growing body of computing research on measuring system fairness and biases associated with data and algorithms, the impact of human biases that go beyond traditional machine learning (ML) pipelines still remain understudied. In this Perspective Paper, we seek to develop a two-sided fairness framework that not only characterizes data and algorithmic biases, but also highlights the cognitive and perceptual biases that may exacerbate system biases and lead to unfair decisions. Within the framework, we also analyze the interactions between human and system biases in search and recommendation episodes. Built upon the two-sided framework, our research synthesizes intervention and intelligent nudging strategies applied in cognitive and algorithmic debiasing, and also proposes novel goals and measures for evaluating the performance of systems in addressing and proactively mitigating the risks associated with biases in data, algorithms, and bounded rationality. This paper uniquely integrates the insights regarding human biases and system biases into a cohesive framework and extends the concept of fairness from human-centered perspective. The extended fairness framework better reflects the challenges and opportunities in users’ interactions with search and recommender systems of varying modalities. Adopting the two-sided approach in information system design has the potential to enhancing both the effectiveness in online debiasing and the usefulness to boundedly rational users engaging in information-intensive decision-making. 

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