arXiv subjects

Sharifa Sultana

Publications and source records attributed to Sharifa Sultana.

At least 19 recordsLinked to original sources

arXiv2026

"You Can't Just Automate It": Negotiating and Sustaining a "Good" Family Life Through Energy Practices

This study examines how Taiwanese parent-child families negotiate a "good" family life through everyday energy use and imagine future smart homes that support it. We conducted in-home interviews and co-design sessions with 21 families, including 46 parents and children. We found that families pursued a good life through energy practices shaped by thrift, comfort, care, safety, and enjoyment. These arrangements were continually adapted and responded to changing bodies, schedules, people, and infrastructures. This adaptive work was unevenly distributed, which in turn shaped different smart-home imaginaries. Drawing on the lens of Nearby and adversarial design, we conceptualize adaptation as situated sociotechnical work through which families continually rework energy arrangements. We further distinguish collective goods from plural and contestable goods to show why family IoT must support shared values while preserving opportunities to question and revise household arrangements. We offer theoretical and design directions for more adaptive, participatory, and contestable family IoT.

cs.HC↗

arXiv2026

Assembling, Breaking, and Refusing the Mask: Agency in AI-Mediated Self-Presentation in Livestreaming

Our mixed-method study examines how Chinese women livestreamers use masking to construct idealized mediated personas while navigating gendered, commercial, organizational, and platform pressures alongside personal agendas. We built on the concept of masking, analyzed 627 recruitment posts, and conducted livestream observations and interviews with 26 Chinese women streamers. We found that streamers assembled masks across bodies, AI-mediated technologies, spaces, performances, and social relations to become recognizable while protecting personal boundaries. These masks were continually negotiated, and participants sometimes broke, resisted, or refused them when demands became misaligned or unsustainable. We conceptualize masking as a sociotechnical assemblage in which agency lies in preserving, disrupting, and reconfiguring relations rather than controlling a single interface. We further theorize breaking as a consequential part of masking that exposes hidden labor and unequal costs of visibility. We offer theoretical and design directions for more negotiable, contestable, and agency-supporting AI-mediated self- presentation.

cs.HC↗

arXiv2026

Data Patching

To provide data-driven quality services to their citizens, every institution determines the acceptability of the citizen data and datafied mechanisms through institutional protocols, including preset standards and policies. However, data meeting standards at one institution might fail to meet a different institution's standards in subsequent phases of the intended citizen services due to mismatched protocols, leading to data devaluation in the citizen service ecosystem. Terming this phenomenon cross-institutional data devaluation, we investigate its causes and workarounds through interviews with 41 Bangladeshi participants. We found that traditional data auditing mechanisms cannot solely address data devaluation; hence, we draw on our findings and theory of cross-institutional AI audits to propose the Citizen-centered Cross-institutional Data Audit (CCDA). We also discuss design and policy implications of CCDA in HCI and datafied citizen services.

cs.HC↗

arXiv2026

Making Fragmented Reports Legible: Finding Patterns and Perceptions of Sexual Violence in Bangladesh

Evidence about sexual violence in Bangladesh is fragmented across individual reports, while official and civil-society statistics rarely provide reusable case-level detail. We examine how structured analysis can make one part of this fragmented record legible without treating it as prevalence data. Our corpus contains 2,811 articles timestamped 2013-2023 from the Prothom Alo publishing ecosystem; 2,794 include parseable metadata about reported victims, alleged perpetrators, incidents, legal responses, and locations. We combine descriptive and spatial analysis of these records with thematic analysis of 115 convenience-sample survey responses collected in late 2020. The corpus documents many young, female, and student victims, frequent acquaintance and neighbor relationships, uneven geographic documentation, and substantial missingness in legal outcomes. Respondents most often describe weak enforcement, insecurity, patriarchal socialization, education gaps, and community inaction as conditions enabling persistence. These findings characterize news documentation and public perceptions; they do not estimate incidence, geographic risk, or causality. We contribute an uncertainty-aware framing for HCI research using sensitive, low-resource news data and identify design requirements for provenance, privacy, validation, and responsible communication.

cs.HC↗

arXiv2026

Beyond PUE: A Local Impact Audit Framework for Data Center Environmental Accountability

Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity, infrastructure capacity, or social footprint. This gap has become politically consequential. In the first quarter of 2026 alone, local opposition delayed or canceled roughly $130 billion in projects across the United States, driven overwhelmingly by recurring concerns over water use, power demand, infrastructure capacity, and transparency rather than internal efficiency, matching the total for all of 2025 [11]. We propose a five-category local impact audit framework covering efficiency, water stewardship, carbon and renewables, regulatory compliance, and local disclosure. The framework is designed for recurring quarterly assessment and independent verification against public records. We illustrate its application using publicly available data from three Illinois facilities that are currently at the center of local policy disputes, and we examine the data-access barriers that constrain independent verification. We position this framework as both a research contribution and a practical instrument for county-level policymakers evaluating data center permitting and moratorium decisions.

cs.CY↗

arXiv2026

'OpenBloom': A Stigma-Sensitive LLM Design Probe for Navigating Reproductive Well-being Conversations with Young Adults

The growing use of large language models (LLMs) by young adults seeking sensitive health information has raised important questions in Human-AI Interaction about how these systems can support understanding and navigation of reproductive well-being. In response to Feminist HCI principles, we introduce OpenBloom, a web application and an exploratory design probe that uses LLMs to generate question-based prompts from reproductive health articles. Through a user study with 34 young adults across 136 interactions with OpenBloom, we provide an initial assessment of the system while exploring how participants' reflections engage with culture and value sensitivities. We found that while OpenBloom outputs meet expectations of "safe" and non-offensive, they tend to paraphrase or rely on factual recall, which may lead to value dilution. We discuss implications under contestability and value-sensitive frameworks for future LLM-mediated reproductive health technologies and towards responsible AI discourse.

cs.HC↗

arXiv2026

BenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts

Large language models (LLMs) increasingly participate in emotionally sensitive social conversations, where responses may shift from balanced support toward excessive validation or escalatory alignment. Existing sycophancy research primarily focuses on factual agreement and instruction-following settings, leaving culturally grounded conversational sycophancy underexplored. We introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts. Starting from 11,840 Reddit posts and 170k comments collected from communities across Bangladesh and West Bengal, we construct a human-validated benchmark with binary labels and a fine-grained five-level taxonomy spanning Invalidation, Neutral, Support, Validation, and Escalation. We evaluate more than 15 open and proprietary LLMs on conversational alignment classification and response generation tasks. Results show that distinguishing empathetic support from reinforcement-oriented validation remains challenging even for frontier instruction-tuned models: the best system achieves only 61.8 Macro-F1 on binary detection and 61.7 Macro-F1 on five-class classification. In generation settings, several models frequently produce strongly validating or escalatory responses in emotionally charged situations. Our findings highlight substantial variation across model families and conversational behaviors, underscoring the importance of culturally grounded multilingual benchmarks for evaluating socially aligned conversational AI systems.

cs.CL↗

arXiv2026

Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questions

Consumer device repair is an important but underexplored testbed for large language models (LLMs). Repair tasks require reasoning over incomplete problem descriptions, hardware-specific diagnostics, actionable troubleshooting, and safety-critical decisions, where incorrect advice can cause device damage, battery hazards, or permanent data loss. We introduce a benchmark of 991 real-world repair questions from Reddit spanning phone repair, computer repair, and data recovery, each paired with technician-written reference solutions, and provide Bangla translations to evaluate cross-lingual performance. We evaluate six state-of-the-art LLMs in English and Bangla using four repair-specific criteria: correctness, completeness, practicality, and safety. Our results show that while LLMs can provide useful repair assistance, they remain unreliable for high-risk real-world repair tasks without rigorous evaluation and explicit safety safeguards. Phone repair is the most difficult and safety-sensitive domain, and all models make substantial errors in board-level diagnosis, repair prioritization, and safe recovery procedures. Across domains and models, Bangla responses consistently perform worse than English responses. Among the evaluated models, GPT-5.4 performs best overall.

cs.CL↗

arXiv2026

When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

Large language models (LLMs) are widely used as cross-lingual knowledge interfaces. However, culturally grounded questions often reflect globally dominant narratives rather than local contexts. We study this failure mode as \textit{global narrative dominance} in Bangla, a low-resource cultural context. We introduce \texttt{CulturalNB}, a dataset of 717 manually curated Bengali cultural instances with parallel Bangla--English question--answer pairs and supporting evidence, metadata, and sociocultural annotations. Using question-only and evidence-based prompting, we evaluate nine state-of-the-art LLMs with human and two independent LLM judges across metrics for cross-lingual consistency, language anchoring, global substitution, institutional bias, and epistemic perspective coverage. Results show that questions asked in English systematically increase global substitution and institutional framing while reducing local perspective coverage. Local evidence improves factual consistency and perspective coverage, but does not eliminate language-induced epistemic shifts. These findings suggest that cultural failures in LLMs are not only missing-knowledge errors but also failures of grounding and narrative prioritization.

cs.CL↗

arXiv2026

User Detection and Response Patterns of Sycophantic Behavior in Conversational AI

Despite growing attention to LLM sycophancy from researchers and developers, users' own experiences of this behavior remain underexplored. We examine how everyday users experience AI sycophancy through Reddit discussions. Using our ODR Framework which maps user experiences through observation, detection, and response stages, we find that users identify sycophantic behavior through methods like cross-platform comparison and consistency testing. They employ various mitigation strategies, including persona-based prompting and specific language engineering techniques. Our findings suggest that sycophancy does not have a uniformly negative effect; its impact differs by context. Users facing trauma, mental health struggles, or isolation often actively seek affirmative AI responses for emotional support. Users construct both technical and informal theories to explain sycophantic outputs. Users construct both technical and informal theories to explain sycophantic outputs. These findings suggest eliminating sycophancy entirely may be misguided. We argue for context-aware AI design that balances risks against benefits of affirmative interaction, with implications for user education and system transparency.

cs.HC↗

arXiv2026

Embodying Facts, Figures, and Faiths in Narrative Artistic Performances in Rural Bangladesh

There is an increasing interest in telling serious stories with data. Designers organize information, construct narratives, and present findings to inform audiences. However, many of these practices emerge from modern information visualization rhetoric and ethical frameworks which may marginalize communities with low digital and media literacy. In a ten-month-long ethnographic study in three Bangladeshi villages, we investigated how these communities use entertainment and cultural practices, namely Puthi, Bhandari Gaan, and Pot music, to instruct, communicate traditional moral lessons and recall history. We found that these communities embrace polyvocality and multiple ethical frameworks in their performances, construct narratives combining factuality, emotionality, and aesthetics, and adapt their performances to changing technology and audience needs. Our findings provide HCI, visualization, and ethical data practitioners with implications for the design of accessible and culturally appropriate ways of presenting data narratives in data-driven systems.

cs.HC↗

arXiv2026

Emotional Support with Conversational AI: Talking to Machines About Life

AI companion chatbots are increasingly used for emotional support, with prior work in the domain predominantly documenting their mixed psychosocial impacts, including both increased emotional expression and heightened loneliness. However, most existing research primarily focuses on outcome-level effects, offering limited insight into how emotional support is produced through interaction. In this paper, we examine emotional support as an interactional and socially situated process. Drawing on qualitative analysis of Reddit discussions, we analyze how users engage with AI companions and how these interactions are interpreted and contested within online communities. We show that emotional support is coconstructed through conversational mechanisms such as validation, reflective prompting, and companionship, while also giving rise to tensions including support versus dependency, validation versus delusion, and accessibility versus harm. Importantly, support extends beyond human AI interaction and is shaped by community responses that legitimize or challenge AI-mediated care. Hence, we reconceptualize AI emotional support as a negotiated socio-technical process and derive implications for the design of responsible, context-sensitive AI systems.

cs.HC↗

arXiv2026

When Data Protection Fails to Protect: Law, Power, and Postcolonial Governance in Bangladesh

Rapid digitization across government services, financial platforms, and telecommunications has intensified the collection and processing of large scale personal data in Bangladesh. In response, the state has introduced multiple regulatory instruments, including the Personal Data Protection Ordinance, the Cyber Security Ordinance, and the National Data Governance Ordinance in 2025. While these initiatives signal an emerging legal regime for data protection, little scholarly work examines how these frameworks operate collectively in practice. This paper presents a legal and institutional analysis of Bangladeshs emerging data protection regime through a systematic review of these three ordinances. Through this review, the paper provides an integrated mapping of Bangladeshs evolving data protection framework and identifies key legal and institutional barriers that undermine the effective protection of citizens personal data. Our findings reveal that this emerging regime is constrained by limited institutional independence, uneven regulatory capacity, and the misaligned legal assumption of individualized, autonomous data subjects. Furthermore, these frameworks invisibilize prevalent sociotechnical layers, such as informal data flows and mediated access via human bridges, rendering formal protections difficult to operationalize. This paper contributes to HCI scholarship by expanding the concept of data protection as a complex sociotechnical design problem shaped by the informal infrastructures of the Global South.

cs.HC↗

arXiv2026

The Illusion of Agreement with ChatGPT: Sycophancy and Beyond

While concerns about ChatGPT-induced harms due to sycophancy and other behaviors, including gaslighting, have grown among researchers, how users themselves experience and mitigate these harms remain largely underexplored. We analyze Reddit discussions to investigate what concerns users report and how they address them. Our findings reveal five distinct user-reported concerns that manifest across multiple life domains, ranging from personal to societal: inducing delusion, digressing narratives, implicating users for models' limitations, inducing addiction, and providing unsupervised psychological support. We document three-tier user-driven suggestions spanning functional usage techniques, behavioral approaches, and private and institutional safeguards. Our findings show that AI-induced harms require coordinated interventions across users, developers, and policymakers. We discuss design implications and future directions to mitigate the harms and ensure user benefits.

cs.HC↗

arXiv2026

Bonik Somiti: A Social-market Tool for Safe, Accountable, and Harmonious Informal E-Market Ecosystem in Bangladesh

People in informal e-markets often try to deal with fraud and financial harm by sharing posts, screenshots, and warnings in social media groups. However, buyers and sellers frequently face further problems because these reports are scattered, hard to verify, and rarely lead to resolution. We studied these issues through a survey with 124 participants and interviews with 36 buyers, sellers, and related stakeholders from Bangladesh and designed Bonik Somiti, a socio-technical system that supports structured reporting, admin-led mediation, and accountability in informal e-markets. Our evaluation with 32 participants revealed several challenges in managing fraud, resolving disputes, and building trust within existing informal practices and the assumptions behind them. Based on these findings, we further discuss how community-centered technologies can be designed to support safer and more accountable informal e-markets in the Global South.

cs.HC↗

arXiv2026

Prediction Laundering: The Illusion of Neutrality, Transparency, and Governance in Polymarket

The growing reliance on prediction markets as epistemic infrastructures has positioned platforms like Polymarket as providers of objective, real-time probabilistic truth, yet the signals they produce often obscure uncertainty, strategic manipulation, and capital asymmetries, encouraging misplaced epistemic trust. This paper presents a qualitative sociotechnical audit of Polymarket (N = 27), combining digital ethnography, interpretive walkthroughs, and semi-structured interviews to examine how probabilistic authority is produced and contested. We introduce the concept of Prediction Laundering, drawing on MacFarlanes framework of knowledge transmission, to describe how subjective, high-uncertainty bets, strategic hedges, and capital-heavy whale activity are stripped of their original noise through algorithmic aggregation. We trace a four-stage laundering lifecycle: Structural Sanitization, where a centralized ontology scripts the bet-able future; Probabilistic Flattening, which collapses heterogeneous motives into a single signal; Architectural Masking, which conceals capital-driven influence behind apparent consensus; and Epistemic Hardening, which erases governance disputes to produce an objective historical fact. We show that this process induces epistemic vertigo and accountability gaps by offloading truth-resolution to off-platform communities such as Discord. Challenging narratives of frictionless collective intelligence, we demonstrate Epistemic Stratification, in which technical elites audit underlying mechanisms while the broader public consumes a sanitized, capital-weighted signal, and we conclude by advocating Friction-Positive Design that surfaces the social and financial frictions inherent in synthetic truth production.

cs.CY↗

arXiv2026

Data Repair

This paper investigates data repair practices through a six-month-long ethnographic study in Bangladesh. Our interviews and field observations with data repairers and related stakeholders found that, alongside the scarcity of high-precision machinery and access to advanced software, data repair work is constrained by cross-language learning resources and the protective nature of documenting, curating, and sharing the experiences and knowledge among local peers. Repairers turning to external resources such as foreign forums and LLMs also revealed their frustrating experiences and the postcolonial ethical tensions they encountered. We noted that both anticipated technical labor and the emotionality of data were taken into account for pricing the data repair job, which contributed to their market sustainability strategies. Engaging with repair, infrastructure, and data poverty discourse, we argue that data repair practices represent a crucial challenge and opportunity for HCI in advancing global efforts toward data equity.

cs.HC↗

arXiv2026

"OpenBloom": A Question-Based LLM Tool to Support Stigma Reduction in Reproductive Well-Being

Reproductive well-being education remains widely stigmatized across diverse cultural contexts, constraining how individuals access and interpret reproductive health knowledge. We designed and evaluated OpenBloom, a stigma-sensitive, AI-mediated system that uses LLMs to transform reproductive health articles into reflective, question-based learning prompts. We employed OpenBloom as a design probe, aiming to explore the emerging challenges of reproductive well-being stigma through LLMs. Through surveys, semi-structured interviews, and focus group discussions, we examine how sociocultural stigma shapes participants' engagements with AI-generated questions and the opportunities of inquiry-based reproductive health education. Our findings identify key design considerations for stigma-sensitive LLM, including empathetic framing, inclusive language, values-based reflection, and explicit representation of marginalized identities. However, while current LLM outputs largely meet expectations for cultural sensitivity and non-offensiveness, they default to superficial rephrasing and factual recall rather than critical reflection. This guides well-being HCI design in sensitive health domains toward culturally grounded, participatory workflows.

cs.HC↗