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“I know even if you don’t tell me”: Understanding Users’ Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationSumit Asthan · 2024Personalization improves user experience by tailoring interactions relevant to each user’s background and preferences. However, per­ sonalization requires information about users that platforms often collect without their awareness or their enthusiastic consent. Here, we study how the transparency of AI inferences on users’ personal data afects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for per­ sonalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly pro­ vided data after awareness of inferences. Our results show that participants chose restrictive consent decisions for sensitive and incorrect inferences about them and for their answers that led to such inferences. Our fndings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences. CCS CONCEPTS • Security and privacy → Social aspects of security and pri­ vacy; • Human-centered computing → Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

How Should AI Systems Talk to Users when Collecting their Personal Information? Efects of Role Framing and Self-Referencing on Human-AI InteractionMengqi Liao · 2021AI systems collect our personal information in order to provide per­ sonalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, which is ded­ icated to improving user experience while promoting informed decision-making: Should the interface tout the benefts of infor­ mation disclosure and frame itself as a help-provider? Or, should it appear as a help-seeker? We decided to fnd out by creating a mockup of a news recommendation system called Mindz and conducting an online user study (N=293) with the following four variations: AI system as help seeker vs. help provider vs. both vs. neither. Data showed that even though all participants received the same recommendations, power users tended to trust a help-seeking Mindz more whereas non-power users favored one that is both help-seeker and help-provider. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

Rise of Machine Agency: A Framework for Studying the Psychology of Human – AI Interaction (HAII)S. Shyam Sundar · 2020*Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.*

UX of AI & PersonalizationResearch Papers

Proactive vs. Reactive Personalization: Can Customization of Privacy Enhance User Experience?Bo Zhang · 2019Online recommender systems have triggered widespread privacy concerns due to their reliance on personal user data for providing personalized services. To address these concerns, some systems have started allowing users to express their preferences before receiving personalized content (i.e., reactive personalization) rather than automatically pushing it to them (i.e., proactive personalization). However, this would mean constant calls for user action, which can adversely affect user experience. One potential solution is to offer users the ability to customize their privacy settings at the outset, thus obviating the need for constant consultation. We conducted a 2 (Personalization: Reactive vs. Proactive) X 3 (Customization of Settings: Absence vs. Action vs. Cue) factorial experiment (N = 299) with a movie recommendation system. Findings show that interface cues suggesting customization enhance user experience, even in the presence of proactive personalization. They also highlight the important role played by negative privacy experiences in the past.

UX of AI & PersonalizationResearch Papers

“This App Would Like to Use Your Current Location to Better Serve You”: Importance of User Assent and System Transparency in Personalized Mobile ServicesTsai-Wei Chen · 2018Modern mobile apps aim to provide personalized services without appearing intrusive. A common strategy is to let the user initiate the service request (e.g., “click here to receive coupons for your favorite products”), a practice known as “overt personalization.” Another strategy is to assuage users’ privacy concerns by being transparent about how their data would be collected, utilized and stored. To test these two strategies, we conducted a 2 (Personalization: Overt vs. Covert) x 2 (Transparency: High vs. Low) factorial experiment, with a fifth control condition. Participants (N=302) interacted with GreenByMe, a prototype of an eco-friendly mobile application. Data show that overt personalization affects perceived control. Significant three-way interactions between power usage, perceived overt personalization and perceived information transparency was seen on perceived ease of use, trust in the app, user engagement and behavioral intention to use the app in the future. In addition, results reveal that perceived information transparency also promotes trust, which is negatively linked with privacy concerns and positively correlated with user engagement and product involvement. Author Keywords Location-Aware Computing; Contextual Computing; Usability Study; User Experience Design; Privacy ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

UX of AI & PersonalizationResearch Papers

Making Sense of Emotion-Sensing: Workshop on Quantifying Human EmotionsBenjamin Tag · 2021The global pandemic and the uncertainty if and when life will return to normality have motivated a series of studies on human mental health. This research has elicited evidence for increasing numbers of anxiety, depression, and overall impaired mental well-being. But, the global COVID-19 pandemic has also created new opportunities for research into quantifying human emotions: remotely, contact- less, in everyday life. The ubiquitous computing community has long been at the forefront of developing, testing, and building user- facing systems that aim at quantifying human emotion. However, rather than aiming at more accurate sensing algorithms, it is time to critically evaluate whether it is actually possible and in what ways it could be beneficial for technologies to be able to detect user emotions. In this workshop, we bring together experts from the fields of Ubiquitous Computing, Human-Computer Interaction, and Psychology to - long-overdue - merge their expertise and ask the fundamental questions: how do we make sense of emotion-sensing, can and should we quantify human emotions? CCS CONCEPTS • Human-centered computing →Human computer interac- tion (HCI); Ubiquitous and mobile computing; • Applied com- puting →Psychology. 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 permissions@acm.org. UbiComp-ISWC ’21 Adjunct, September 21–26, 2021, Virtual, USA © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-8461-2/21/09...$15.00 https://doi.org/10.1145/3460418.3479272

Emotional DesignResearch Papers

Affective computing: challenges2003A number of researchers around the world have built machines that recognize, express, model, communicate, and respond to emotional information, instances of ‘‘affective computing.’’ This article raises and responds to several criticisms of affective computing, articulating state-of-the art research challenges, especially with respect to affect in human- computer interaction. r 2003 Elsevier Science Ltd. All rights reserved.

Emotional DesignResearch Papers