arXiv subjects

Samantha Dalal

Publications and source records attributed to Samantha Dalal.

12 recordsLinked to original sources

arXiv2026

StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs

Multimodal large language models (MLLMs) are increasingly deployed in personally and societally consequential settings, yet the visual cues that shape how these models judge people remain poorly understood. Prior work often compares different (groups of) individuals, making it difficult to separate appearance effects from identity differences. We introduce StylisticBias, a controlled benchmark for evaluating attribute-level social bias in MLLMs. We generate 500 photorealistic base faces and create about 50 single-attribute variations per face, producing about 25K images. This design keeps identity fixed and changes one visual attribute at a time. It lets us measure how specific cues shift model judgments. We evaluate six MLLMs across 25 binary social judgment scenarios. We find that age and body type dominate identity-level effects, while fashion style and other visual cues drive the largest attribute-level shifts. We further find that about 15 attributes account for nearly 80\% of the total variation, showing that bias is concentrated in a small set of visual cues. Sensitivity is strongest in judgments that are semantically aligned with appearance, especially socioeconomic and style-related judgments. We release StylisticBias as a benchmark for fine-grained bias evaluation in multimodal models. Code and dataset: https://github.com/timo-cavelius/StylisticBias and https://hf.co/datasets/shaghayegh/stylistic-bias-dataset.

cs.CL↗

arXiv2026

Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics

Measurement is essential to improving AI performance and mitigating harms for marginalized groups. As generative AI systems are rapidly deployed across geographies and contexts, AI measurement practices must be designed to support repeatable, automatable application across different models, datasets, and evaluation settings. But the drive to automate measurement can be in tension with the ability for measurement instruments to capture the expertise and perspectives of communities impacted by AI. Recent work advocates for breaking measurement into several key stages: first moving from an abstract concept to be measured into a precise, "systematized" concept; next operationalizing the systematized concept into a concrete measurement instrument; and finally applying the measurement instrument on data to produce measurements. This opens up an opportunity to concentrate community engagement in the systematization phase before operationalizing and applying measurement instruments. In this paper, we explore how to involve communities in systematizing the concept of "cultural appropriateness" in text-to-image models' representation of culturally significant artifacts through case studies with three communities: blind and low vision individuals residing in the UK, residents of Kerala, and residents of Tamil Nadu. Our systematized concepts reflect community members' lived experiences interacting with each artifact and how they want their material culture to be depicted, demonstrating the value of community involvement in defining valid measures. We explore how these systematized concepts can be operationalized into automated measurement instruments that could be applied using a multimodal LLM-as-a-judge approach and challenges that remain. We reflect on the benefits and limitations of such approaches.

cs.CY↗

arXiv2025

The Human Labour of Data Work: Capturing Cultural Diversity through World Wide Dishes

This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, community-led image and text dataset of culinary dishes and associated cultural customs. We present WWD as an example of participatory dataset creation, where community members both guide the design of the research process and contribute to the crowdsourced dataset. This approach incorporates localised expertise and knowledge to address the limitations of web-scraped Internet datasets acknowledged in the Participatory AI discourse. We show that our approach can result in curated, high-quality data that supports decentralised contributions from communities that do not typically contribute to datasets due to a variety of systemic factors. Our project demonstrates the importance of participatory mediators in supporting community engagement by identifying the kinds of labour they performed to make WWD possible. We surface three dimensions of labour performed by participatory mediators that are crucial for participatory dataset construction: building trust with community members, making participation accessible, and contextualising community values to support meaningful data collection. Drawing on our findings, we put forth five lessons for building infrastructure to support future participatory AI efforts.

cs.CY↗

arXiv2025

FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers

Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collaborated with a Colorado-based rideshare union to develop FairFare, a tool that crowdsources and analyzes workers' data to estimate the take rate -- the percentage of the rider price retained by the rideshare platform. We deployed FairFare with our partner organization that collaborated with us in collecting data on 76,000+ trips from 45 drivers over 18 months. During evaluation interviews, organizers reported that FairFare helped influence the bill language and passage of Colorado Senate Bill 24-75, calling for greater transparency and data disclosure of platform operations, and create a national narrative. Finally, we reflect on complexities of translating quantitative data into policy outcomes, nature of community based audits, and design implications for future transparency tools.

cs.HC↗

arXiv2025

The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation

We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes evaluation dataset. We also analyse bias operationalisation to highlight how current systems underperform across several dimensions: (in-)accuracy, (mis-)representation, and cultural (in-)sensitivity, with evidence from qualitative community-based observations and quantitative automated tools. We find that these T2I models generally do not produce quality outputs of dishes specific to various regions. This is true even for the US, which is typically considered more well-resourced in training data -- although the generation of US dishes does outperform that of the investigated African countries. The models demonstrate the propensity to produce inaccurate and culturally misrepresentative, flattening, and insensitive outputs. These representational biases have the potential to further reinforce stereotypes and disproportionately contribute to erasure based on region. The dataset and code are available at https://github.com/oxai/world-wide-dishes.

cs.CY↗

arXiv2025

FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations

What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers, through a 6 month partnership with the State of Washington's largest rideshare labor union. Over the following 3 months, our field deployment of FareShare registered 178 account signups. We observed that the tool could reduce lost wage calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.

cs.CY↗

arXiv2025

QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums

Online discussion forums provide crucial data to understand the concerns of a wide range of real-world communities. However, the typical qualitative and quantitative methodologies used to analyze those data, such as thematic analysis and topic modeling, are infeasible to scale or require significant human effort to translate outputs to human readable forms. This study introduces QuaLLM, a novel LLM-based framework to analyze and extract quantitative insights from text data on online forums. The framework consists of a novel prompting and human evaluation methodology. We applied this framework to analyze over one million comments from two of Reddit's rideshare worker communities, marking the largest study of its type. We uncover significant worker concerns regarding AI and algorithmic platform decisions, responding to regulatory calls about worker insights. In short, our work sets a new precedent for AI-assisted quantitative data analysis to surface concerns from online forums.

cs.CL↗

arXiv2025

Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy

Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designers, and practitioners take to mitigate these negative impacts and meet worker needs? In this paper, we identify transparency-related harms, mitigation strategies, and worker needs while validating and contextualizing our findings within the broader worker community. We use a novel mixed-methods study combining an LLM-based analysis of over 1 million comments posted to online platform worker communities with semi-structured interviews with workers. Our findings expose a transparency gap between existing platform designs and the information drivers need, particularly concerning promotions, fares, routes, and task allocation. Our analysis suggests that rideshare workers need key pieces of information, which we refer to as indicators, to make informed work decisions. These indicators include details about rides, driver statistics, algorithmic implementation details, and platform policy information. We argue that instead of relying on platforms to include such information in their designs, new regulations requiring platforms to publish public transparency reports may be a more effective solution to improve worker well-being. We offer recommendations for implementing such a policy.

cs.CY↗

arXiv2024

Provocation: Who benefits from "inclusion" in Generative AI?

The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures are paramount to to ensure non-dominant values, knowledge and material culture are also reflected in AI models and the media they generate, we argue that dominant structures of community participation in AI development and evaluation are not explicit enough about the benefits and harms that members of socially marginalized groups may experience as a result of their participation. Without explicit interrogation of these benefits by AI developers, as a community we may remain blind to the immensity of systemic change that is needed as well. To support this provocation, we present a speculative case study, developed from our own collective experiences as AI researchers. We use this speculative context to itemize the barriers that need to be overcome in order for the proposed benefits to marginalized communities to be realized, and harms mitigated.

cs.CY↗

arXiv2024

Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities

Platform-based laborers face unprecedented challenges and working conditions that result from algorithmic opacity, insufficient data transparency, and unclear policies and regulations. The CSCW and HCI communities increasingly turn to worker data collectives as a means to advance related policy and regulation, hold platforms accountable for data transparency and disclosure, and empower the collective worker voice. However, fundamental questions remain for designing, governing and sustaining such data infrastructures. In this workshop, we leverage frameworks such as data feminism to design sustainable and power-aware data collectives that tackle challenges present in various types of online labor platforms (e.g., ridesharing, freelancing, crowdwork, carework). While data collectives aim to support worker collectives and complement relevant policy initiatives, the goal of this workshop is to encourage their designers to consider topics of governance, privacy, trust, and transparency. In this one-day session, we convene research and advocacy community members to reflect on critical platform work issues (e.g., worker surveillance, discrimination, wage theft, insufficient platform accountability) as well as to collaborate on codesigning data collectives that ethically and equitably address these concerns by supporting working collectivism and informing policy development.

cs.HC↗

arXiv2023

Understanding Human Intervention in the Platform Economy: A case study of an indie food delivery service

This paper examines the sociotechnical infrastructure of an "indie" food delivery platform. The platform, Nosh, provides an alternative to mainstream services, such as Doordash and Uber Eats, in several communities in the Western United States. We interviewed 28 stakeholders including restauranteurs, couriers, consumers, and platform administrators. Drawing on infrastructure literature, we learned that the platform is a patchwork of disparate technical systems held together by human intervention. Participants join this platform because they receive greater agency, financial security, and local support. We identify human intervention's key role in making food delivery platform users feel respected. This study provides insights into the affordances, limitations, and possibilities of food delivery platforms designed to prioritize local contexts over transnational scales.

cs.HC↗

arXiv2022

"Hey, Can You Add Captions?": The Critical Infrastructuring Practices of Neurodiverse People on TikTok

Accessibility efforts, how we can make the world usable and useful to as many people as possible, have explicitly focused on how we can support and allow for the autonomy and independence of people with disabilities, neurotypes, chronic conditions, and older adults. Despite these efforts, not all technology is designed or implemented to support everyone's needs. Recently, a community-organized push by creators and general users of TikTok urged the platform to add accessibility features, such as closed captioning to user-generated content, allowing more people to use the platform with greater ease. Our work focuses on an understudied population -- people with ADHD and those who experience similar challenges -- exploring the creative practices people from this community engage in, focusing on the kinds of accessibility they create through their creative work. Through an interview study exploring the experiences of creatives on TikTok, we find that creatives engage in critical infrastructuring -- a process of bottom-up (re)design -- to make the platform more accessible despite the challenges the platform presents to them as creators. We present these critical infrastructuring practices through the themes of: creating and augmenting video editing infrastructures and creating and augmenting video captioning infrastructures. We reflect on the introduction of a top-down infrastructure - the implementation of an auto-captioning feature - shifts the critical infrastructure practices of content creators. Through their infrastructuring, creatives revised sociotechnical capabilities of TikTok to support their own needs as well as the broader needs of the TikTok community. We discuss how the routine of infrastructuring accessibility is actually best conceptualized as incidental care work. We further highlight how accessibility is an evolving sociotechnical construct, and forward the concept of contextual accessibility.

cs.HC↗