arXiv subjects

Pamela Wisniewski

Publications and source records attributed to Pamela Wisniewski.

27 records · Page 2Linked to original sources

arXiv2024

Towards Collaborative Family-Centered Design for Online Safety, Privacy and Security

Traditional online safety technologies often overly restrict teens and invade their privacy, while parents often lack knowledge regarding their digital privacy. As such, prior researchers have called for more collaborative approaches on adolescent online safety and networked privacy. In this paper, we propose family-centered approaches to foster parent-teen collaboration in ensuring their mobile privacy and online safety while respecting individual privacy, to enhance open discussion and teens' self-regulation. However, challenges such as power imbalances and conflicts with family values arise when implementing such approaches, making parent-teen collaboration difficult. Therefore, attending the family-centered design workshop will provide an invaluable opportunity for us to discuss these challenges and identify best research practices for the future of collaborative online safety and privacy within families.

cs.HC↗

arXiv2024

Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation

In this position paper, we discuss the potential for leveraging LLMs as interactive research tools to facilitate collaboration between human coders and AI to effectively annotate online risk data at scale. Collaborative human-AI labeling is a promising approach to annotating large-scale and complex data for various tasks. Yet, tools and methods to support effective human-AI collaboration for data annotation are under-studied. This gap is pertinent because co-labeling tasks need to support a two-way interactive discussion that can add nuance and context, particularly in the context of online risk, which is highly subjective and contextualized. Therefore, we provide some of the early benefits and challenges of using LLMs-based tools for risk annotation and suggest future directions for the HCI research community to leverage LLMs as research tools to facilitate human-AI collaboration in contextualized online data annotation. Our research interests align very well with the purposes of the LLMs as Research Tools workshop to identify ongoing applications and challenges of using LLMs to work with data in HCI research. We anticipate learning valuable insights from organizers and participants into how LLMs can help reshape the HCI community's methods for working with data.

cs.HC↗

arXiv2024

Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery

Following the recent release of various Artificial Intelligence (AI) based Conversation Agents (CAs), adolescents are increasingly using CAs for interactive knowledge discovery on sensitive topics, including mental and sexual health topics. Exploring such sensitive topics through online search has been an essential part of adolescent development, and CAs can support their knowledge discovery on such topics through human-like dialogues. Yet, unintended risks have been documented with adolescents' interactions with AI-based CAs, such as being exposed to inappropriate content, false information, and/or being given advice that is detrimental to their mental and physical well-being (e.g., to self-harm). In this position paper, we discuss the current landscape and opportunities for CAs to support adolescents' mental and sexual health knowledge discovery. We also discuss some of the challenges related to ensuring the safety of adolescents when interacting with CAs regarding sexual and mental health topics. We call for a discourse on how to set guardrails for the safe evolution of AI-based CAs for adolescents.

cs.HC↗

arXiv2024

Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation

Online dating platforms have gained widespread popularity as a means for individuals to seek potential romantic relationships. While recommender systems have been designed to improve the user experience in dating platforms by providing personalized recommendations, increasing concerns about fairness have encouraged the development of fairness-aware recommender systems from various perspectives (e.g., gender and race). However, sexual orientation, which plays a significant role in finding a satisfying relationship, is under-investigated. To fill this crucial gap, we propose a novel metric, Opposite Gender Interaction Ratio (OGIR), as a way to investigate potential unfairness for users with varying preferences towards the opposite gender. We empirically analyze a real online dating dataset and observe existing recommender algorithms could suffer from group unfairness according to OGIR. We further investigate the potential causes for such gaps in recommendation quality, which lead to the challenges of group quantity imbalance and group calibration imbalance. Ultimately, we propose a fair recommender system based on re-weighting and re-ranking strategies to respectively mitigate these associated imbalance challenges. Experimental results demonstrate both strategies improve fairness while their combination achieves the best performance towards maintaining model utility while improving fairness.

cs.IR↗

arXiv2024

Examining the Unique Online Risk Experiences and Mental Health Outcomes of LGBTQ+ versus Heterosexual Youth

We collected and analyzed Instagram direct messages (DMs) from 173 youth aged 13-21 (including 86 LGBTQ+ youth). We examined youth's risk-flagged social media trace data with their self-reported mental health outcomes to examine how the differing online experiences of LGBTQ+ youth compare with their heterosexual counterparts. We found that LGBTQ+ youth experienced significantly more high-risk online interactions compared to heterosexual youth. LGBTQ+ youth reported overall poorer mental health, with online harassment specifically amplifying Self-Harm and Injury. LGBTQ+ youth's mental well-being linked positively to sexual messages, unlike heterosexual youth. Qualitatively, we found that most of the risk-flagged messages of LGBTQ+ youth were sexually motivated; however, a silver lining was that they sought support for their sexual identity from peers on the platform. The study highlights the importance of tailored online safety and inclusive design for LGBTQ+ youth, with implications for CHI community advancements in fostering a supportive online environments.

cs.HC↗

arXiv2023

Algorithmic Robustness

Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform. Below, we motivate the importance of algorithmic robustness, present a conceptual framework, and highlight the relevant areas of research for which algorithmic robustness is relevant. Why robustness? Robustness is an important enabler of other goals that are frequently cited in the context of public policy decisions about computational systems, including trustworthiness, accountability, fairness, and safety. Despite this dependence, it tends to be under-recognized compared to these other concepts. This is unfortunate, because robustness is often more immediately achievable than these other ultimate goals, which can be more subjective and exacting. Thus, we highlight robustness as an important goal for researchers, engineers, regulators, and policymakers when considering the design, implementation, and deployment of computational systems. We urge researchers and practitioners to elevate the attention paid to robustness when designing and evaluating computational systems. For many key systems, the immediate question after any demonstration of high performance should be: "How robust is that performance to realistic changes in the task or environment?" Greater robustness will set the stage for systems that are more trustworthy, accountable, fair, and safe. Toward that end, this document provides a brief roadmap to some of the concepts and existing research around the idea of algorithmic robustness.

cs.AI↗

arXiv2023

Older and younger adults are influenced differently by dark pattern designs

Considering that prior research has found older users undergo a different privacy decision-making process compared to younger adults, more research is needed to inform the behavioral privacy disclosure effects of these strategies for different age groups. To address this gap, we used an existing dataset of an experiment with a photo-tagging Facebook application. This experiment had a 2x2x5 between-subjects design where the manipulations were common dark pattern design strategies: framing (positive vs. negative), privacy defaults (opt-in vs. opt-out), and justification messages (positive normative, negative normative, positive rationale, negative rationale, none). We compared older (above 65 years old, N=44) and young adults (18 to 25 years old, N=162) privacy concerns and disclosure behaviors (i.e., accepting or refusing automated photo tagging) in the scope of dark pattern design. Overall, we find support for the effectiveness of dark pattern designs in the sense that positive framing and opt-out privacy defaults significantly increased disclosure behavior, while negative justification messages significantly decreased privacy concerns. Regarding older adults, our results show that certain dark patterns do lead to more disclosure than for younger adults, but also to increased privacy concerns for older adults than for younger.

cs.HC↗

arXiv2022

A User Study to Evaluate a Web-based Prototype for Smart Home Internet of Things Device Management

With the growing advances in the Internet of Things (IoT) technology, IoT device management platforms are becoming increasingly important. We conducted a web-based survey and usability study with 43 participants who use IoT devices frequently to: 1) examine their smart home IoT usage patterns and privacy preferences, and 2) evaluate a web-based prototype for smart home IoT device management. We found that participants perceived privacy as more important than the convenience afforded by the IoT devices. Based on their average scores of privacy vs. convenience importance, participants with low privacy and low convenience significantly reported less privacy control and convenience preferences than participants with high privacy and high convenience. Overall, all participants were satisfied with the proposed website prototype and their actual usability evaluation demonstrated a good understanding of the website features. This paper provides an empirical examination of the privacy versus convenience trade-offs smart home users make when managing their IoT devices.

cs.HC↗

arXiv2021

A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare

Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between \textit{human discretion}, \textit{bureaucratic processes}, and \textit{algorithmic decision-making} by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework to conduct a qualitative analysis of an in-depth, eight-month ethnographic case study of the algorithms in daily use within a child-welfare agency that serves approximately 900 families and 1300 children in the mid-western United States. Overall, we found there is a need to focus on strength-based algorithmic outcomes centered in social ecological frameworks. In addition, algorithmic systems need to support existing bureaucratic processes and augment human discretion, rather than replace it. Finally, collective buy-in in algorithmic systems requires trust in the target outcomes at both the practitioner and bureaucratic levels. As a result of our study, we propose guidelines for the design of high-stakes algorithmic decision-making tools in the child-welfare system, and more generally, in the public sector. We empirically validate the theoretically derived ADMAPS framework to demonstrate how it can be useful for systematically making pragmatic decisions about the design of algorithms for the public sector.

cs.HC↗