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Ashlee Milton

Publications and source records attributed to Ashlee Milton.

8 recordsLinked to original sources
arXiv2026

"Always Want to Use it for Everything": Understanding Young Adults' Perceptions of AI Dependence

The growing integration of general-purpose AI chatbots into people's daily lives has raised concerns about the potential for unhealthy dependence, particularly among young adults. As a first step toward understanding and characterizing AI chatbot dependence from the perspective of young adults, we collected testimonials from AI chatbot users aged 18 to 25 through an online questionnaire to capture their thoughts and experiences with this phenomenon. From participant responses, we identified three contributing factors of AI dependence: chronic use, efficiency, and delegation. The combination of these in a person's interaction behavior was considered to indicate AI dependence. Participants also observed feelings of atrophy in abilities from AI dependence, leading to psychological impacts such as feelings of inadequacy. Interpreting these findings through the lens of self-determination theory reveals how AI chatbot dependence can impact young adults' personal and social development. We argue that preventing lasting harm to young adults' development is paramount, and provide implications for rethinking AI chatbot dependence grounded in this understanding.

cs.HC↗
arXiv2026

Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

cs.HC↗
arXiv2025

"I'm Petting the Laptop, Which Has You Inside It": Reflecting on Lived Experiences of Online Friendship

Online(-only) friendships have become increasingly common in daily lives post-COVID despite debates around their mental health benefits and equivalence to ''real'' relationships. Previous research has reflected a need to understand how online friends engage beyond individual platforms, and the lack of platform-agnostic inquiry limits our ability to fully understand the dynamics of online friendship. We employed an activity-grounded analysis of 25 interviews on lived experiences of close online friendship spanning multiple years. Our findings present unique challenges and strategies in online friendships, such as stigma from real-life circles, an ambivalent relationship with online communities, and counter-theoretical reappropriations of communication technology. This study contributes to HCI research in online communities and social interface design by refocusing prior impressions of strong vs. weak-ties in online social spaces and foregrounding time-stable interactions in design for relationship maintenance through technology. Our work also promotes critical reflection on biased perspectives towards technology-mediated practices and consideration of online friends as an invisible marginalized community.

cs.HC↗
arXiv2024

The Human Factor in AI Red Teaming: Perspectives from Social and Collaborative Computing

Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how red teamers are selected, biases and blindspots in how tests are conducted, and harmful content's psychological effects on red teamers. A growing body of HCI and CSCW literature examines related practices-including data labeling, content moderation, and algorithmic auditing. However, few, if any have investigated red teaming itself. Future studies may explore topics ranging from fairness to mental health and other areas of potential harm. We aim to facilitate a community of researchers and practitioners who can begin to meet these challenges with creativity, innovation, and thoughtful reflection.

cs.HC↗
arXiv2021

To Infinity and Beyond! Accessibility is the Future for Kids' Search Engines

Research in the area of search engines for children remains in its infancy. Seminal works have studied how children use mainstream search engines, as well as how to design and evaluate custom search engines explicitly for children. These works, however, tend to take a one-size-fits-all view, treating children as a unit. Nevertheless, even at the same age, children are known to possess and exhibit different capabilities. These differences affect how children access and use search engines. To better serve children, in this vision paper, we spotlight accessibility and discuss why current research on children and search engines does not, but should, focus on this significant matter.

cs.IR↗
arXiv2021

Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations

In this position paper, we argue for the need to investigate if and how gender stereotypes manifest in search and recommender systems.As a starting point, we particularly focus on how these systems may propagate and reinforce gender stereotypes through their results in learning environments, a context where teachers and children in their formative stage regularly interact with these systems. We provide motivating examples supporting our concerns and outline an agenda to support future research addressing the phenomena.

cs.IR↗
arXiv2020

Evaluating Information Retrieval Systems for Kids

Evaluation of information retrieval systems (IRS) is a prominent topic among information retrieval researchers--mainly directed at a general population. Children require unique IRS and by extension different ways to evaluate these systems, but as a large population that use IRS have largely been ignored on the evaluation front. In this position paper, we explore many perspectives that must be considered when evaluating IRS; we specially discuss problems faced by researchers who work with children IRS, including lack of evaluation frameworks, limitations of data, and lack of user judgment understanding.

cs.IR↗