Sharifa Sultana

Pratyasha Saha, Anita Say Chan, Sharifa Sultana · utoronto

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.

Atm Mizanur Rahman, Md Arid Hasan, Syed Ishtiaque Ahmed et al.

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.

Md Arid Hasan, Ruwad Naswan, Farhan Samir et al.

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.

Kazi Noshin, Syed Ishtiaque Ahmed, Sharifa Sultana

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.

Ramaravind Kommiya Mothilal, Faisal M. Lalani, Syed Ishtiaque Ahmed et al. · utoronto

The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows. This leads to confusion about what assumptions are and what needs to be done with them. We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions. Through semi-structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded. Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.