huashen218/convxai-cia-dataset huggingface dataset:
Title: Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications
Authors: family: Gamage
given: Dilrukshi; family: Ghasiya
given: Piyush; family: Bonagiri
given: Vamshi; family: Whiting
given: Mark E.; family: Sasahara
given: Kazutoshi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: topic modeling, content analysis, deepfake, societal implication

Abstract: Deepfakes are synthetic content generated using advanced deep learning and AI technologies. The advancement of technology has created opportunities for anyone to create and share deepfakes much easier. This may lead to societal concerns based on how communities engage with it. However, there is limited research available to understand how communities perceive deepfakes. We examined deepfake conversations on Reddit from 2018 to 2021—including major topics and their temporal changes as well as implications of these conversations. Using a mixed-method approach—topic modeling and qualitative coding, we found 6,638 posts and 86,425 comments discussing concerns of the believable nature of deepfakes and how platforms moderate them. We also found Reddit conversations to be pro-deepfake and building a community that supports creating and sharing deepfake artifacts and building a marketplace regardless of the consequences. Possible implications derived from qualitative codes indicate that deepfake conversations raise societal concerns. We propose that there are implications for Human Computer Interaction (HCI) to mitigate the harm created from deepfakes.
Title: Caught in the Network: The Impact of WhatsApp’s 2021 Privacy Policy Update on Users’ Messaging App Ecosystems
Authors: family: Griggio
given: Carla F.; family: Nouwens
given: Midas; family: Klokmose
given: Clemens Nylandsted
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Privacy, Policy, Messaging Apps, Interoperability, Ecosystems of Communication Apps, Computer-Mediated Communication (CMC), Competition Regulation, WhatsApp

Abstract: In January 2021, WhatsApp announced an update to their privacy policy, sparking an outcry that saw millions of users install other messaging apps such as Telegram and Signal. This presented a rare opportunity to study users’ experiences when trying to leave the world’s most popular communication app. We conducted surveys in February and May with 1525 WhatsApp users from Mexico, Spain, South Africa, and the United Kingdom. Over a quarter wanted to switch at least part of their communication to other apps, but 74% of them failed to do so. By May, 27% had increased their use of other apps, and only 16% used WhatsApp less. Beyond network effects, users struggled with making informed choices of alternative apps and with differences in their design and functionality. We suggest messaging interoperability as an approach to alleviate switching costs and discuss implications for HCI research and competition regulation of digital services.
Title: ”Ask this from the person who has private stuff”: Privacy Perceptions, Behaviours and Beliefs Beyond W.E.I.R.D
Authors: family: Naveed
given: Sheza; family: Naveed
given: Hamza; family: Javed
given: Mobin; family: Mustafa
given: Maryam
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: religion, patriarchy, privacy, South Asia, gender

Abstract: We explore privacy perceptions, beliefs and practices of low-literate, low-income users in Pakistan, a patriarchal and religious context with a literacy rate of approx. 68% and where 59% of mobile users have less than 6 years of formal education. Through a qualitative study with 40 participants (17 male and 23 female) we examine the cultural, religious, and familial structures that impact users perceptions, management, and control of their personal privacy. We reveal significant gendered differences in privacy understandings, privacy preserving practices and the access to privacy related knowledge. Our work also highlights the seminal impact religious beliefs have on men and women’s understandings and management of privacy and the prolific use of after-market modified apps to support users specific privacy needs. The privacy concerns raised by our participants provide HCI researchers with valuable insights into designing privacy affordances for vulnerable and diverse populations beyond Western, educated, industrialized, rich and democratic contexts.
Title: Human-GDPR Interaction: Practical Experiences of Accessing Personal Data
Authors: family: Bowyer
given: Alex; family: Holt
given: Jack; family: Go Jefferies
given: Josephine; family: Wilson
given: Rob; family: Kirk
given: David; family: David Smeddinck
given: Jan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: privacy, HDI, trust, data portability, digital rights, open data, information access, user empowerment, personal data, information literacy, GDPR, human-data interaction, participatory action research, data collection

Abstract: In our data-centric world, most services rely on collecting and using personal data. The EU's General Data Protection Regulation (GDPR) aims to enhance individuals’ control over their data, but its practical impact is not well understood. We present a 10-participant study, where each participant filed 4-5 data access requests. Through interviews accompanying these requests and discussions scrutinising returned data, it appears that GDPR falls short of its goals due to non-compliance and low-quality responses. Participants found their hopes to understand providers’ data practices or harness their own data unmet. This causes increased distrust without any subjective improvement in power, although more transparent providers do earn greater trust. We propose designing more effective, data-inclusive and open policies and data access systems to improve both customer relations and individual agency, and also that wider public use of GDPR rights could help with delivering accountability and motivating providers to improve data practices.
Title: XR-OOM: MiXed Reality driving simulation with real cars for research and design
Authors: family: Goedicke
given: David; family: Bremers
given: Alexandra W.D.; family: Lee
given: Sam; family: Bu
given: Fanjun; family: Yasuda
given: Hiroshi; family: Ju
given: Wendy
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: XR, user studies, design, safety, automotive, mixed reality, driving simulation

Abstract: High-fidelity driving simulators can act as testbeds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. In this system paper, we develop and validate the safety of a mixed reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. To this end, we have validated the mixed reality system for basic driver cockpit and low-speed driving tasks, comparing the use of the system with non-headset and with the headset driving conditions, to ensure that participants behave and perform similarly using this system as they would otherwise. This paper outlines the operational procedures and protocols for using such systems for cockpit tasks (like using the parking brake, reading the instrument panel, and turn signaling) as well as basic low-speed driving exercises (such as steering around corners, weaving around obstacles, and stopping at a fixed line) in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in real-world driving tasks.
Title: ControllerPose: Inside-Out Body Capture with VR Controller Cameras
Authors: family: Ahuja
given: Karan; family: Shen
given: Vivian; family: Fang
given: Cathy Mengying; family: Riopelle
given: Nathan; family: Kong
given: Andy; family: Harrison
given: Chris
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Pose Tracking, Virtual Reality, Motion Capture

Abstract: We present a new and practical method for capturing user body pose in virtual reality experiences: integrating cameras into handheld controllers, where batteries, computation and wireless communication already exist. By virtue of the hands operating in front of the user during many VR interactions, our controller-borne cameras can capture a superior view of the body for digitization. Our pipeline composites multiple camera views together, performs 3D body pose estimation, uses this data to control a rigged human model with inverse kinematics, and exposes the resulting user avatar to end user applications. We developed a series of demo applications illustrating the potential of our approach and more leg-centric interactions, such as balancing games and kicking soccer balls. We describe our proof-of-concept hardware and software, as well as results from our user study, which point to imminent feasibility.
Title: Enabling Tangible Interaction on Non-touch Displays with Optical Mouse Sensor and Visible Light Communication
Authors: family: Yan
given: Yihui; family: Huang
given: Zezhe; family: Xudu
given: Feiyang; family: Yang
given: Zhice
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Tangible, Visible Light Communication, Optical Mouse, Tabletop

Abstract: This paper presents Centaur, an input system that enables tangible interaction on displays, e.g., untouchable computer monitors. Centaur’s tangibles are built from low-cost optical mouse sensors, or can alternatively be emulated by commercial optical mice already available. They are trackable when put on the display, rendering a real-time and high-precision tangible interface. Even for ordinary personal computers, enabling Centaur requires no new hardware and installation burden. Centaur’s cost-effectiveness and wide availability open up new opportunities for tangible user interface (TUI) users and practitioners. Centaur’s key innovation lies in its tracking method. It embeds high-frequency light signals into different portions of the display content as location beacons. When the tangibles are put on the screen, they are able to sense the light signals with their optical mouse sensors, and thus determine the locations accordingly. We develop four applications to showcase the potential usage of Centaur.
Title: Embr: A Creative Framework for Hand Embroidered Liquid Crystal Textile Displays
Authors: family: Endow
given: Shreyosi; family: Rakib
given: Mohammad Abu Nasir; family: Srivastava
given: Anvay; family: Rastegarpouyani
given: Sara; family: Torres
given: Cesar
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: e-textiles, craft, making

Abstract: Conductive thread is a common material in e-textile toolkits that allows practitioners to create connections between electronic components sewn on fabric. When powered, conductive threads are used as resistive heaters to activate thermochromic dyes or pigments on textiles to create interactive, aesthetic, and ambient textile displays. In this work, we introduce Embr, a creative framework for supporting hand-embroidered liquid crystal textile displays (LCTDs). This framework includes a characterization of conductive embroidery stitches, an expanded repertoire of thermal formgiving techniques, and a thread modeling tool used to simulate mechanical, thermal, and electrical behaviors of LCTDs. Through exemplar artifacts, we annotate a morphological design space of LCTDs and discuss the tensions and opportunities of satisfying the wider range of electrical, craft, cultural, aesthetic, and functional concerns inherent to e-textile practices.
Title: ASTEROIDS: Exploring Swarms of Mini-Telepresence Robots for Physical Skill Demonstration
Authors: family: Li
given: Jiannan; family: Sousa
given: Maurício; family: Li
given: Chu; family: Liu
given: Jessie; family: Chen
given: Yan; family: Balakrishnan
given: Ravin; family: Grossman
given: Tovi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Collaboration, Physical Skill, Robots, Telepresence

Abstract: Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ physical presence while using tangible manipulations to control the audience’s viewpoints and presentation flow. We conducted an exploratory evaluation for Asteroids with 12 remote participants in a model-making tutorial scenario with an architectural expert demonstrator. Results suggest our unique features benefitted participants’ engagement, sense of presence, and understanding.
Title: Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques
Authors: family: Chan
given: Liwei; family: Liao
given: Yi-Chi; family: Mo
given: George B; family: Dudley
given: John J; family: Cheng
given: Chun-Lien; family: Kristensson
given: Per Ola; family: Oulasvirta
given: Antti
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Touch, Interface Design, Bayesian Optimization, Human-in-the-loop Optimization, Haptics, Multi-objective Optimization

Abstract: Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.
Title: For What It’s Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems
Authors: family: Erlei
given: Alexander; family: Das
given: Richeek; family: Meub
given: Lukas; family: Anand
given: Avishek; family: Gadiraju
given: Ujwal
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Online Experiment, Human-AI Interaction, Ultimatum Bargaining, Market Interaction, AI system, Decision Support System

Abstract: As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent’s behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm.
Title: You Complete Me: Human-AI Teams and Complementary Expertise
Authors: family: Zhang
given: Qiaoning; family: Lee
given: Matthew L; family: Carter
given: Scott
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: trust, complementary expertise, human-AI teams, explainable AI

Abstract: People consider recommendations from AI systems in diverse domains ranging from recognizing tumors in medical images to deciding which shoes look cute with an outfit. Implicit in the decision process is the perceived expertise of the AI system. In this paper, we investigate how people trust and rely on an AI assistant that performs with different levels of expertise relative to the person, ranging from completely overlapping expertise to perfectly complementary expertise. Through a series of controlled online lab studies where participants identified objects with the help of an AI assistant, we demonstrate that participants were able to perceive when the assistant was an expert or non-expert within the same task and calibrate their reliance on the AI to improve team performance. We also demonstrate that communicating expertise through the linguistic properties of the explanation text was effective, where embracing language increased reliance and distancing language reduced reliance on AI.
Title: Jury Learning: Integrating Dissenting Voices into Machine Learning Models
Authors: family: Gordon
given: Mitchell L.; family: Lam
given: Michelle S.; family: Park
given: Joon Sung; family: Patel
given: Kayur; family: Hancock
given: Jeff; family: Hashimoto
given: Tatsunori; family: Bernstein
given: Michael S.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Abstract: Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups’ labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier’s prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators’ models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent. A field evaluation finds that practitioners construct diverse juries that alter 14% of classification outcomes.
Title: What is Your Current Mindset?
Authors: family: Viswanathan
given: Sruthi; family: Omidvar-Tehrani
given: Behrooz; family: Renders
given: Jean-Michel
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: POI recommendation, Category-based interface, Multi-objective optimisation., urban exploration, Mindsets, Satisficing, POI exploration

Abstract: Is recommendation the new search? Recommender systems have shortened the search for information in everyday activities such as following the news, media, and shopping. In this paper, we address the challenges of capturing the situational needs of the user and linking them to the available datasets with the concept of Mindsets. Mindsets are categories such as “I’m hungry” and “Surprise me” designed to lead the users to explicitly state their intent, control the recommended content, save time, get inspired, and gain shortcuts for a satisficing exploration of POI recommendations. In our methodology, we first compiled Mindsets with a card sorting workshop and a formative evaluation. Using the insights gathered from potential end users, we then quantified Mindsets by linking them to POI utility measures using approximated lexicographic multi-objective optimisation. Finally, we ran a summative evaluation of Mindsets and derived guidelines for designing novel categories for recommender systems.
Title: Designing and Assessing a Virtual Reality Simulation to Build Resilience to Street Harassment
Authors: family: Jicol
given: Crescent; family: Feltham
given: Julia; family: Yoon
given: Jinha; family: Proulx
given: Michael J; family: O'Neill
given: Eamonn; family: Lutteroth
given: Christof
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: street harassment, ethics, virtual reality, design

Abstract: Street harassment is a widespread problem that can constrain people’s freedom to enjoy public spaces safely, along with many other negative psychological impacts. However, very little research has looked at how immersive technology can help in addressing it. We conducted three studies to investigate the design decisions, ethical issues and efficacy of an immersive simulation of street harassment: an online design study (n=20), an interview study with experts working in the area (n=9), and a comparative lab study investigating design, ethics and efficacy (n=44). Our results deepen understanding of the design decisions that contribute to a realistic psychological experience, such as the effects of screen-based video vs passive VR vs interactive VR. They also highlight important ethical issues such as traumatisation and potential for victim blaming, and how they can be approached in an ethical manner. Finally, they provide insights into efficacy in terms of perceived usefulness, competence and empathy.