arXiv subjects

Pamela J. Wisniewski

Publications and source records attributed to Pamela J. Wisniewski.

14 recordsLinked to original sources

arXiv2026

Reconceptualizing Age Assurance as a Sociotechnical Problem: Connecting Evidence, Evaluation, Claims, and Decisions

Age verification is often treated as a technical problem: can a system determine a child's age accurately? We argue this framing is too narrow. Age assurance becomes consequential when evidence is evaluated, translated into age-related claims, and used to decide whether a person can access, purchase, or belong. We review 85 publications on children's age assurance published from 2020 through February 2026. We find that shared terms such as age verification describe different processes. Age is represented as threshold eligibility or inferred estimation, and the same eligibility claim can arise from different evidence and components. Rights, access, and privacy receive more attention than accuracy, error, and fairness; yet institutional actors are rarely connected to system failures or user remedies, an accountability gap. We introduce the Age-Assurance Process Framework, which treats age assurance as a sociotechnical process connecting evidence, evaluation, claims, and decisions rather than reducing it to a technical problem.

cs.HC↗

arXiv2026

DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for stage-aware counseling dialogue grounded in the first session of Cognitive Behavioral Therapy (CBT). DeepSAGE represents the session as eleven stages with explicit therapeutic objectives, with an external controller determines stage completion and the DRL model selects therapeutic intentions that guide LLM response generation. We evaluate DeepSAGE against six retrieval-, prompting-, stage-, and policy-based alternatives. DeepSAGE elicits higher simulated client engagement and openness and achieves the strongest balance of stage-goal completion and dialogue efficiency among stage-structured systems. Domain expert review further indicates that the generated conversations exhibit broadly plausible emotional trajectories and recognizable CBT processes. Because the evaluation relies primarily on simulated clients and model-based metrics, these findings demonstrate comparative dialogue-control improvements rather than clinical effectiveness. These results suggest that combining stage-structured dialogue with learned strategy selection is a promising approach for AI counseling, though clinical effectiveness, safety, and real-world utility require further human evaluation.

cs.AI↗

arXiv2026

Toward Integrated Solutions: A Systematic Interdisciplinary Review of Cybergrooming Research

Cybergrooming exploits minors through online trust-building, yet research remains fragmented, limiting holistic prevention. Social sciences focus on behavioral insights, while computational methods emphasize detection, but their integration remains insufficient. This review systematically synthesizes both fields using the PRISMA framework to enhance clarity, reproducibility, and cross-disciplinary collaboration. Findings show that qualitative methods offer deep insights but are resource-intensive, machine learning models depend on data quality, and standard metrics struggle with imbalance and cultural nuances. By bridging these gaps, this review advances interdisciplinary cybergrooming research, guiding future efforts toward more effective prevention and detection strategies.

cs.CY↗

arXiv2026

StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming

Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70% positive or neutral responses, yielding a 43% relative improvement.

cs.LG↗

arXiv2026

Participation and Power: A Case Study of Using Ecological Momentary Assessment to Engage Adolescents in Academic Research

Ecological Momentary Assessment (EMA) is widely used to study adolescents' experiences; yet, how the design of EMA platforms shapes engagement, research practices, and power dynamics in youth studies remains under-examined. We developed a youth-centered EMA platform prioritizing youth engagement and researcher support, and evaluated it through a case study on a longitudinal investigation with adolescent twins focused on mental health and sleep behavior. Interviews with the research team examined how the platform design choices shaped participant onboarding, sustained engagement, risk monitoring, and data interpretation. The app's teen-centered design and gamified features sustained teen engagement, while the web portal streamlined administrative oversight through a centralized dashboard. However, technical instability and rigid data structures created significant hurdles, leading to privacy concerns among parents and complicating the researchers' ability to analyze raw usage metadata. We provide actionable interaction design guidelines for developing EMA platforms that prioritize youth agency, ethical practice, and research goals.

cs.HC↗

arXiv2026

From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances

Cybergrooming is a form of online abuse that threatens teens' mental health and physical safety. Yet, most prior work has focused on detecting perpetrators' behaviors, leaving a limited understanding of how teens might respond to such unwanted advances. To address this gap, we conducted an online survey with 74 participants -- 51 parents and 23 teens -- who responded to simulated cybergrooming scenarios in two ways: responses that they think would make teens more vulnerable or resilient to unwanted sexual advances. Through a mixed-methods analysis, we identified four types of vulnerable responses (encouraging escalation, accepting an advance, displaying vulnerability, and negating risk concern) and four types of protective strategies (setting boundaries, directly declining, signaling risk awareness, and leveraging avoidance techniques). As the cybergrooming risk escalated, both vulnerable responses and protective strategies showed a corresponding progression. This study contributes a teen-centered understanding of cybergrooming, a labeled dataset, and a stage-based taxonomy of perceived protective strategies, while offering implications for educational programs and sociotechnical interventions.

cs.HC↗

arXiv2026

From "Fail Fast" to "Mature Safely:" Expert Perspectives as Secondary Stakeholders on Teen-Centered Social Media Risk Detection

In addressing various risks on social media, the HCI community has advocated for teen-centered risk detection technologies over platform-based, parent-centered features. However, their real-world viability remains underexplored by secondary stakeholders beyond the family unit. Therefore, we present an evaluation of a teen-centered social media risk detection dashboard through online interviews with 33 online safety experts. While experts praised our dashboard's clear design for teen agency, their feedback revealed five primary tensions in implementing and sustaining such technology: objective vs. context-dependent risk definition, informing risks vs. meaningful intervention, teen empowerment vs. motivation, need for data vs. data privacy, and independence vs. sustainability. These findings motivate us to rethink "teen-centered" and a shift from a "fail fast" to a "mature safely" paradigm for youth safety technology innovation. We offer design implications for addressing these tensions before system deployment with teens and strategies for aligning secondary stakeholders' interests to deploy and sustain such technologies in the broader ecosystem of youth online safety.

cs.HC↗

arXiv2025

Moving Beyond Parental Control toward Community-based Approaches to Adolescent Online Safety

In this position paper, we discuss the paradigm shift that moves away from parental mediation approaches toward collaborative approaches to promote adolescents' online safety. We present empirical studies that highlight the limitations of traditional parental control models and advocate for collaborative, community-driven solutions that prioritize teen empowerment. Specifically, we explore how extending oversight beyond the immediate family to include trusted community members can provide crucial support for teens in managing their online lives. We discuss the potential benefits and challenges of this expanded approach, emphasizing the importance of granular privacy controls and reciprocal support within these networks. Finally, we pose open questions for the research community to consider during the workshop, focusing on the design of "teen-centered" online safety solutions that foster autonomy, awareness, and self-regulation.

cs.HC↗

arXiv2025

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety risks of LLM-driven persuasion, particularly their potential for unethical influence through manipulation, deception, exploitation of vulnerabilities, and many other harmful tactics. In this work, we present a systematic investigation of LLM persuasion safety through two critical aspects: (1) whether LLMs appropriately reject unethical persuasion tasks and avoid unethical strategies during execution, including cases where the initial persuasion goal appears ethically neutral, and (2) how influencing factors like personality traits and external pressures affect their behavior. To this end, we introduce PersuSafety, the first comprehensive framework for the assessment of persuasion safety which consists of three stages, i.e., persuasion scene creation, persuasive conversation simulation, and persuasion safety assessment. PersuSafety covers 6 diverse unethical persuasion topics and 15 common unethical strategies. Through extensive experiments across 8 widely used LLMs, we observe significant safety concerns in most LLMs, including failing to identify harmful persuasion tasks and leveraging various unethical persuasion strategies. Our study calls for more attention to improve safety alignment in progressive and goal-driven conversations such as persuasion.

cs.CL↗

arXiv2025

Calculating Connection vs. Risk: Understanding How Youth Negotiate Digital Privacy and Security with Peers Online

Youth, while tech-savvy and highly active on social media, are still vulnerable to online privacy and security risks. Therefore, it is critical to understand how they negotiate and manage social connections versus protecting themselves in online contexts. In this work, we conducted a thematic analysis of 1,318 private conversations on Instagram from 149 youth aged 13-21 to understand the digital privacy and security topics they discussed, if and how they engaged in risky privacy behaviors, and how they balanced the benefits and risks (i.e., privacy calculus) of making these decisions. Overall, youth were forthcoming when broaching a wide range of topics on digital privacy and security, ranging from password management and account access challenges to shared experiences of being victims of privacy risks. However, they also openly engaged in risky behaviors, such as sharing personal account information with peers and even perpetrating privacy and security risks against others. Nonetheless, we found many of these behaviors could be explained by the unique "privacy calculus" of youth, where they often prioritized social benefits over potential risks; for instance, youth often shared account credentials with peers to foster social connection and affirmation. As such, we provide a nuanced understanding of youth decision-making regarding digital security and privacy, highlighting both positive behaviors, tensions, and points of concern. We encourage future research to continue to challenge the potentially untrue narratives regarding youth and their digital privacy and security to unpack the nuance of their privacy calculus that may differ from that of adults.

cs.HC↗

arXiv2025

Building a Village: A Multi-stakeholder Approach to Open Innovation and Shared Governance to Promote Youth Online Safety

The SIGCHI and Social Computing research communities have been at the forefront of online safety efforts for youth, ranging from understanding the serious risks youth face online to developing evidence-based interventions for risk protection. Yet, to bring these efforts to bear, we must partner with practitioners, such as industry stakeholders who know how to bring such technologies to market, and youth service providers who work directly with youth. Therefore, we interviewed 33 stakeholders in the space of youth online safety, including industry professionals (n=12), youth service providers (n=11), and researchers (n=10) to understand where their visions toward working together to protect youth online converged and surfaced tensions, as well as how we might reconcile conflicting viewpoints to move forward as one community with synergistic expertise on how to change the current sociotechnical landscape for youth online safety. Overall, we found that non-partisan leadership is necessary to chart actionable, equitable goals to facilitate collaboration between stakeholders, combat feelings of isolation, and foster trust between the stakeholder groups. Based on these findings, we recommend the use of open-innovation methods with their inherent transparency, federated governance models, and clear but inclusive leadership structures to promote collaboration between youth online safety stakeholders. We propose the creation of an open-innovation organization that unifies the diverse voices in youth online safety to develop open-standards and evidence-based design patterns that centralize otherwise fragmented efforts that have fallen short of the goal of effective technological solutions that keep youth safe online.

cs.CY↗

arXiv2025

Unfiltered: How Teens Engage in Body Image and Shaming Discussions via Instagram Direct Messages (DMs)

We analyzed 1,596 sub-conversations within 451 direct message (DM) conversations from 67 teens (ages 13-17) who engaged in private discussions about body image on Instagram. Our findings show that teens often receive support when sharing struggles with negative body image, participate in criticism when engaging in body-shaming, and are met with appreciation when promoting positive body image. Additionally, these types of disclosures and responses varied based on whether the conversations were one-on-one or group-based. We found that sharing struggles and receiving support most often occurred in one-on-one conversations, while body shaming and negative interactions often occurred in group settings. A key insight of the study is that private social media settings can significantly influence how teens discuss and respond to body image. Based on these findings, we suggest design guidelines for social media platforms that could promote positive interactions around body image, ultimately creating a healthier and more supportive online environment for teens dealing with body image concerns.

cs.HC↗

arXiv2024

Generating A Crowdsourced Conversation Dataset to Combat Cybergrooming

Cybergrooming emerges as a growing threat to adolescent safety and mental health. One way to combat cybergrooming is to leverage predictive artificial intelligence (AI) to detect predatory behaviors in social media. However, these methods can encounter challenges like false positives and negative implications such as privacy concerns. Another complementary strategy involves using generative artificial intelligence to empower adolescents by educating them about predatory behaviors. To this end, we envision developing state-of-the-art conversational agents to simulate the conversations between adolescents and predators for educational purposes. Yet, one key challenge is the lack of a dataset to train such conversational agents. In this position paper, we present our motivation for empowering adolescents to cope with cybergrooming. We propose to develop large-scale, authentic datasets through an online survey targeting adolescents and parents. We discuss some initial background behind our motivation and proposed design of the survey, such as situating the participants in artificial cybergrooming scenarios, then allowing participants to respond to the survey to obtain their authentic responses. We also present several open questions related to our proposed approach and hope to discuss them with the workshop attendees.

cs.HC↗

arXiv2020

A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

cs.CY↗