arXiv subjects

Dipto Das

Publications and source records attributed to Dipto Das.

At least 19 recordsLinked to original sources

arXiv2026

Palimpsestic Accountability in the US Federal AI Inventories

An AI inventory is a structured, often public record in which a government or organization lists and describes the AI systems it uses or plans to use. Studies of single AI inventory releases describe what systems do but say little about how they are governed over time. We linked entries across three releases of the US Federal AI Use Case Inventory (2023--2025), compared answers only where both releases required them, and analyzed governance accounts through a dual deductive-inductive approach, guided by the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework. As the inventory grew from 710 to 3,611 entries from 2023 to 2025, fewer required answers were left blank, but accountability was rewritten through three mechanisms. Each release \emph{redrew} the scope of the questions; agency-assigned classifications \emph{gated} what was reported; and answers were also \emph{overwritten}, with gains and losses concentrated in single agencies and little account of human oversight and judgment. These findings show that each release redraws the boundary of accountability and leaves the public to \emph{re-stitch} the records, a condition we term \textbf{palimpsestic accountability}. We recommend versioned records, reasons for every reclassification, public files that distinguish kinds of missing answers, and oversight documented as conditions to improve public sector AI accountability.

cs.AI↗

arXiv2026

A Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories

Artificial intelligence (AI) registers and inventories aim to make governmental AI visible, but their institutional scope, schemas, and reporting practices construct different representations of public-sector AI. We compare 8,368 records from country-specific and transnational inventories covering 72 countries. Across 23 harmonized fields, registers shared a descriptive core but rarely requested information about appeals, risks, legal bases, or external evaluation. We found that broad schemas often contained substantial missingness, schema similarity showed no significant patterned convergence, and multiple sources covering the same jurisdictions overlapped only selectively. Based on these findings, we synthesize a layered visibility framework that shows how register records reflect disclosure arrangements and why interoperability requires shared concepts, clear definitions, and preserved provenance.

cs.AI↗

arXiv2026

AMINA: The Inclusive and Accountable AI for Marginalized Immigrant Nonprofit Assistance

Immigrant-led nonprofit groups, particularly those operating in politically sensitive contexts, face exclusion from formal registries and digital platforms. This paper reports a three-phase mixed-methods study with Iranian immigrant nonprofit practitioners: 27 semi-structured interviews, a co-design session, and 7 evaluation and feedback interviews on a prototyped AI assistant, AMINA. Our findings highlight how legitimacy barriers, capacity gaps, and politically charged misinformation constrain nonprofit operations. We translate these insights into design goals for an inclusive nonprofit AI assistant: support for everyday group operations, recognition of informal nonprofit efforts, proactive countering of misinformation, and multilingual, accessible interaction. User evaluations show AMINAs potential to reduce reporting burdens and foster transparency through proactive reminders, and catalyze collaboration across dispersed networks. We contribute to CSCW and HCI by characterizing the cooperative work of transnational immigrant nonprofits, extending scholarship on informality and misinformation, and demonstrating how AI can act as a collaborative partner that strengthens, rather than displaces, the human connections at the core of nonprofit ecosystems, while also posing major risks.

cs.CY↗

arXiv2026

Generative AI Alignment with Hinduism's Theological Plurality and Sacred Representation

Generative AI systems are increasingly used to answer personal questions and mediate everyday practices, including religion. However, existing discussions around AI alignment and ethics have largely centered secular, Western, and Abrahamic assumptions about religion, offering limited attention to other faith-based traditions. In this paper, we examine how Hindu users engage with generative AI systems in relation to their religious knowledge, belief, and practice. Drawing on 15 semi-structured interviews with Bangladeshi Hindu participants, we analyze how users interpret AI-generated religious representations, scriptural explanations, devotional interactions, and synthetic religious media. We found that AI can be both accessible and ethically troubling. While AI supported scriptural inquiry, devotional visualization, and religious storytelling, our study also identified concerns about theological flattening, cultural misrepresentation, devotional manipulation, and the simulation of sacred presence and authority. We conclude by arguing that religious alignment in generative AI requires interpretive alignment: systems that disclose their limits, preserve plurality, and avoid simulating sacred authority and sycophantic personalization.

cs.AI↗

arXiv2026

Decolonial Discourse in Postcolonial Contexts: How YouTubers Negotiate Audience Tensions, Platform Governance, and State Influence

Decolonial discourse on online platforms is often framed in terms of creator motivations and expressive possibilities. In this paper, we examine what it takes to sustain such discourse under layered sociotechnical constraints. Drawing on semi-structured interviews with YouTubers engaging in Bengali decolonial discourse, we analyze how audience publics, platform governance, and state influences shape what becomes sayable, visible, and viable. We show how fragmented postcolonial identities among audiences produce legitimacy policing, harassment, and coordinated backlash, requiring ongoing relational labor from creators. At the platform level, differential monetization, opaque moderation, and copyright regimes reorganize which publics are economically viable and reinforce existing hierarchies. Further, intermediaries such as multi-channel networks mediate regulatory pressure, introducing political risks and constraints on participation. In response, content creators engage in strategies of negotiation, including boundary work, infrastructural improvisation, and multi-platform distribution. Overall, our findings highlight the layered dynamics of decolonial discourse in postcolonial contexts and the continuous work required to sustain it in platformed environments.

cs.HC↗

arXiv2026

Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics

Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate across disciplines less, suggesting an epistemic tension towards human methods. We propose recommendations for establishing the epistemic fit of human research within AISE and bridging the prohibitive limitations that researchers face, while avoiding performative 'human-washing'.

cs.CY↗

arXiv2026

Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online. As content moderation increasingly depends on large language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive speech-language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility. Focusing on Bangladesh's Hindu and Chakma communities -- the country's largest religious and Indigenous ethnic minorities, respectively -- this paper investigates the epistemic limits of LLM-based moderation systems and explores methods for incorporating minority perspectives. We co-created a culturally grounded corpus of insensitive speech with community members and integrated their narratives into moderation pipelines using retrieval augmented generation (RAG). Our tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience. Through mixed-method evaluations involving both minority and majority participants, we demonstrate that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines. This work advances research in human-computer interaction, AI ethics, and social computing by foregrounding restorative justice and hermeneutical inclusion in the design of content moderation systems.

cs.HC↗

arXiv2026

"Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System

This paper examines how algorithmic accountability in Canada's visa system is articulated institutionally and experienced by applicants across borders. We analyzed Immigration, Refugees and Citizenship Canada (IRCC)'s Algorithmic Impact Assessment (AIA) for the temporary resident visa (TRV) triage system using the algorithmic decision-making adapted for the public sector (ADMAPS) framework and analyzed Reddit discussions among applicants using a mixed-methods approach. We show that while institutional artifacts emphasize transparency, procedural safeguards, and bounded impacts, applicants engage in collective sensemaking to interpret opaque decisions, often relying on peer knowledge amid uncertainty. We identify three asymmetries between how institutional accountability is structured and how people perceive the process: epistemic asymmetry in access to decision logic, jurisdictional asymmetry in exposure shaped by geopolitical positioning, and temporal--relational asymmetry in how waiting and uncertainty are experienced. We emphasize why it is important to shift attention from institutional design to the uneven distribution of experiences with public-sector algorithmic governance. Together, these contributions demonstrate how algorithmic governance systems in the context of transnational migration produce structured asymmetries not captured by institutional disclosure frameworks, and how extending ADMAPS can account for those uneven translations of accountability.

cs.CY↗

arXiv2026

How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language

Sociotechnical systems, such as language technologies, frequently exhibit identity-based biases. These biases exacerbate the experiences of historically marginalized communities and remain understudied in low-resource contexts. While models and datasets specific to a language or with multilingual support are commonly recommended to address these biases, this paper empirically tests the effectiveness of such approaches in the context of gender, religion, and nationality-based identities in Bengali, a widely spoken but low-resourced language. We conducted an algorithmic audit of sentiment analysis models built on mBERT and BanglaBERT, which were fine-tuned using all Bengali sentiment analysis (BSA) datasets from Google Dataset Search. Our analyses showed that BSA models exhibit biases across different identity categories despite having similar semantic content and structure. We also examined the inconsistencies and uncertainties arising from combining pre-trained models and datasets created by individuals from diverse demographic backgrounds. We connected these findings to the broader discussions on epistemic injustice, AI alignment, and methodological decisions in algorithmic audits.

cs.CL↗

arXiv2026

Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models

Text-to-Image (T2I) models have shown promising utility across various domains. However, such models are also amplifying harmful societal biases in their outputs. In the context of South Asia, recent work has shown caste biases and stereotypes are being perpetuated through Generative AI (GenAI) systems. While this research offers extremely relevant insight into invisibilized narratives of caste discrimination through the GenAI system, they often treat caste as an identity category. Therefore, in this work we shift our ontology to focus on the relational aspect of caste. This enables us to develop a more nuanced understanding of the mechanics of caste discrimination by and through T2I models. Combining an algorithmic audit with critical discourse analysis, we draw on a conceptual frame challenging Brahminical Normativity to show how caste biases are perpetuated beyond the simple binaries of upper vs lower-caste categories. Our contributions are two-fold. Beyond challenging the categorical understanding of caste as a category, we propose an anti-caste approach to tackle the issue of caste bias and fairness in AI systems.

cs.CY↗

arXiv2026

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines

Fairness audits of institutional risk models are critical for understanding how deployed machine learning pipelines allocate resources. Drawing on multi-year collaboration with Centennial College, where our prior ethnographic work introduced the ASP-HEI Cycle, we present a replica-based audit of a deployed Early Warning System (EWS), replicating its model using institutional training data and design specifications. We evaluate disparities by gender, age, and residency status across the full pipeline (training data, model predictions, and post-processing) using standard fairness metrics. Our audit reveals systematic misallocation: younger, male, and international students are disproportionately flagged for support, even when many ultimately succeed, while older and female students with comparable dropout risk are under-identified. Post-processing amplifies these disparities by collapsing heterogeneous probabilities into percentile-based risk tiers. This work provides a replicable methodology for auditing institutional ML systems and shows how disparities emerge and compound across stages, highlighting the importance of evaluating construct validity alongside statistical fairness. It contributes one empirical thread to a broader program investigating algorithms, student data, and power in higher education.

cs.CY↗

arXiv2026

Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures

In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we argue that such registers are not neutral mirrors of government activity, but active instruments of ontological design that configure the boundaries of accountability. We analyzed the Register's complete dataset of 409 systems using the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework, combining quantitative mapping with deductive qualitative coding. Our findings reveal a sharp divergence between the rhetoric of "sovereign AI" and the reality of bureaucratic practice: while 86\% of systems are deployed internally for efficiency, the Register systematically obscures the human discretion, training, and uncertainty management required to operate them. By privileging technical descriptions over sociotechnical context, the Register constructs an ontology of AI as "reliable tooling" rather than "contestable decision-making." We conclude that without a shift in design, such transparency artifacts risk automating accountability into a performative compliance exercise, offering visibility without contestability.

cs.AI↗

arXiv2026

Tracing Users' Privacy Concerns Across the Lifecycle of a Romantic AI Companion

Romantic AI chatbots have quickly attracted users, but their emotional use raises concerns about privacy and safety. As people turn to these systems for intimacy, comfort, and emotionally significant interaction, they often disclose highly sensitive information. Yet the privacy implications of such disclosure remain poorly understood in platforms shaped by persistence, intimacy, and opaque data practices. In this paper, we examine public Reddit discussions about privacy in romantic AI chatbot ecosystems through a lifecycle lens. Analyzing 2,909 posts from 79 subreddits collected over one year, we identify four recurring patterns: disproportionate entry requirements, intensified sensitivity in intimate use, interpretive uncertainty and perceived surveillance, and irreversibility, persistence, and user burden. We show that privacy in romantic AI is best understood as an evolving socio-technical governance problem spanning access, disclosure, interpretation, retention, and exit. These findings highlight the need for privacy and safety governance in romantic AI that is staged across the lifecycle of use, supports meaningful reversibility, and accounts for the emotional vulnerability of intimate human-AI interaction.

cs.CY↗

arXiv2026

How do the Global South Diasporas Mobilize for Transnational Political Change?

This paper examines how non-resident Bangladeshis mobilized during the 2024 quota-reform turned pro-democracy movement, leveraging social platforms and remittance flows to challenge state authority. Drawing on semi-structured interviews, we identify four phases of their collective action: technology-mediated shifts to active engagement, rapid transnational network building, strategic execution of remittance boycott, reframing economic dependence as political leverage, and adaptive responses to government surveillance and information blackouts. We extend postcolonial computing by introducing the idea of "diasporic superposition," which shows how diasporas can exercise political and economic influence from hybrid positionalities that both contest and complicate power asymmetries. We reframe diaspora engagement by highlighting how migrants participate in and reshape homeland politics, beyond narratives of integration in host countries. We advance the scholarship on financial technologies by foregrounding their relationship with moral economies of care, state surveillance, regulatory constraints, and uneven international economic power dynamics. Together, these contributions theorize how transnational activism and digital technologies intersect to mobilize political change in Global South contexts.

cs.CY↗

arXiv2025

BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communities

Understanding political discourse in online spaces is crucial for analyzing public opinion and ideological polarization. While social computing and computational linguistics have explored such discussions in English, such research efforts are significantly limited in major yet under-resourced languages like Bengali due to the unavailability of datasets. In this paper, we present a multilingual dataset of Bengali transnational political discourse (BTPD) collected from three online platforms, each representing distinct community structures and interaction dynamics. Besides describing how we hand-curated the dataset through community-informed keyword-based retrieval, this paper also provides a general overview of its topics and multilingual content.

cs.CL↗

arXiv2025

The Datafication of Care in Public Homelessness Services

Homelessness systems in North America adopt coordinated data-driven approaches to efficiently match support services to clients based on their assessed needs and available resources. AI tools are increasingly being implemented to allocate resources, reduce costs and predict risks in this space. In this study, we conducted an ethnographic case study on the City of Toronto's homelessness system's data practices across different critical points. We show how the City's data practices offer standardized processes for client care but frontline workers also engage in heuristic decision-making in their work to navigate uncertainties, client resistance to sharing information, and resource constraints. From these findings, we show the temporality of client data which constrain the validity of predictive AI models. Additionally, we highlight how the City adopts an iterative and holistic client assessment approach which contrasts to commonly used risk assessment tools in homelessness, providing future directions to design holistic decision-making tools for homelessness.

cs.HC↗

arXiv2024

The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW's literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication.

cs.HC↗

arXiv2024

A Civics-oriented Approach to Understanding Intersectionally Marginalized Users' Experience with Hate Speech Online

While content moderation in online platforms marginalizes users in the Global South at large, users of certain identities are further marginalized. Such users often come from Indigenous ethnic minority groups or identify as women. Through a qualitative study based on 18 semi-structured interviews, this paper explores how such users' experiences with hate speech online in Bangladesh are shaped by their intersectional identities. Through a civics-oriented approach, we examined the spectrum of their legal status, membership, rights, and participation as users of online platforms. Drawing analogies with the concept of citizenship, we develop the concept of usership that offers a user-centered metaphor in studying moderation and platform governance.

cs.HC↗