arXiv subjects

Motahhare Eslami

Publications and source records attributed to Motahhare Eslami.

At least 37 records · Page 2Linked to original sources

arXiv2025

MIRAGE: Multi-model Interface for Reviewing and Auditing Generative Text-to-Image AI

While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and usability in different applications. Recent years have seen growing interest in engaging diverse AI users in auditing generative AI that might impact their lives. To this end, we propose MIRAGE as a web-based tool where AI users can compare outputs from multiple AI text-to-image (T2I) models by auditing AI-generated images, and report their findings in a structured way. We used MIRAGE to conduct a preliminary user study with five participants and found that MIRAGE users could leverage their own lived experiences and identities to surface previously unnoticed details around harmful biases when reviewing multiple T2I models' outputs compared to reviewing only one.

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arXiv2025

Vipera: Towards systematic auditing of generative text-to-image models at scale

Generative text-to-image (T2I) models are known for their risks related such as bias, offense, and misinformation. Current AI auditing methods face challenges in scalability and thoroughness, and it is even more challenging to enable auditors to explore the auditing space in a structural and effective way. Vipera employs multiple visual cues including a scene graph to facilitate image collection sensemaking and inspire auditors to explore and hierarchically organize the auditing criteria. Additionally, it leverages LLM-powered suggestions to facilitate exploration of unexplored auditing directions. An observational user study demonstrates Vipera's effectiveness in helping auditors organize their analyses while engaging with diverse criteria.

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arXiv2025

Investigating Youth AI Auditing

Youth are active users and stakeholders of artificial intelligence (AI), yet they are often not included in responsible AI (RAI) practices. Emerging efforts in RAI largely focus on adult populations, missing an opportunity to get unique perspectives of youth. This study explores the potential of youth (teens under the age of 18) to engage meaningfully in RAI, specifically through AI auditing. In a workshop study with 17 teens, we investigated how youth can actively identify problematic behaviors in youth-relevant ubiquitous AI (text-to-image generative AI, autocompletion in search bar, image search) and the impacts of supporting AI auditing with critical AI literacy scaffolding with guided discussion about AI ethics and an auditing tool. We found that youth can contribute quality insights, shaped by their expertise (e.g., hobbies and passions), lived experiences (e.g., social identities), and age-related knowledge (e.g., understanding of fast-moving trends). We discuss how empowering youth in AI auditing can result in more responsible AI, support their learning through doing, and lead to implications for including youth in various participatory RAI processes.

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arXiv2025

Emerging Practices in Participatory AI Design in Public Sector Innovation

Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design - or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.

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arXiv2025

"It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. Although prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how internal cross-functional collaboration around AI unfolds between AI professionals and journalists within the news industry. Through interviews with 17 journalists, six AI technologists, and three AI workers with cross-functional experience from leading Chinese news organizations, we investigate the practices, challenges, and opportunities for internal cross-functional collaboration around AI in news industry. We first study how these journalists and AI professionals perceive existing internal cross-collaboration strategies. We explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry.

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arXiv2025

A Systematic Literature Review on Equity and Technology in HCI and Fairness: Navigating the Complexities and Nuances of Equity Research

Equity is crucial to the ethical implications in technology development. However, implementing equity in practice comes with complexities and nuances. In response, the research community, especially the human-computer interaction (HCI) and Fairness community, has endeavored to integrate equity into technology design, addressing issues of societal inequities. With such increasing efforts, it is yet unclear why and how researchers discuss equity and its integration into technology, what research has been conducted, and what gaps need to be addressed. We conducted a systematic literature review on equity and technology, collecting and analyzing 202 papers published in HCI and Fairness-focused venues. Amidst the substantial growth of relevant publications within the past four years, we deliver three main contributions: (1) we elaborate a comprehensive understanding researchers' motivations for studying equity and technology, (2) we illustrate the different equity definitions and frameworks utilized to discuss equity, (3) we characterize the key themes addressing interventions as well as tensions and trade-offs when advancing and integrating equity to technology. Based on our findings, we elaborate an equity framework for researchers who seek to address existing gaps and advance equity in technology.

cs.CY↗

arXiv2024

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. The results of comparative assessments indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines, airways, and vascular structures. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

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arXiv2024

'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants

The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development--e.g., through surveys, experiments, and interviews--with synthetic research data generated by large language models (LLMs). We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift. Initially skeptical, researchers were surprised to see similar narratives emerge in the LLM-generated data when using the interview probe. However, over several conversational turns, they went on to identify fundamental limitations, such as how LLMs foreclose participants' consent and agency, produce responses lacking in palpability and contextual depth, and risk delegitimizing qualitative research methods. We argue that the use of LLMs as proxies for participants enacts the surrogate effect, raising ethical and epistemological concerns that extend beyond the technical limitations of current models to the core of whether LLMs fit within qualitative ways of knowing.

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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.

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arXiv2024

Children's Overtrust and Shifting Perspectives of Generative AI

The capabilities of generative AI (genAI) have dramatically increased in recent times, and there are opportunities for children to leverage new features for personal and school-related endeavors. However, while the future of genAI is taking form, there remain potentially harmful limitations, such as generation of outputs with misinformation and bias. We ran a workshop study focused on ChatGPT to explore middle school girls' (N = 26) attitudes and reasoning about how genAI works. We focused on girls who are often disproportionately impacted by algorithmic bias. We found that: (1) middle school girls were initially overtrusting of genAI, (2) deliberate exposure to the limitations and mistakes of generative AI shifted this overtrust to disillusionment about genAI capabilities, though they were still optimistic for future possibilities of genAI, and (3) their ideas about school policy were nuanced. This work informs how children think about genAI like ChatGPT and its integration in learning settings.

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arXiv2024

Public Technologies Transforming Work of the Public and the Public Sector

Technologies adopted by the public sector have transformed the work practices of employees in public agencies by creating different means of communication and decision-making. Although much of the recent research in the future of work domain has concentrated on the effects of technological advancements on public sector employees, the influence on work practices of external stakeholders engaging with this sector remains under-explored. In this paper, we focus on a digital platform called OneStop which is deployed by several building departments across the U.S. and aims to integrate various steps and services into a single point of online contact between public sector employees and the public. Drawing on semi-structured interviews with 22 stakeholders, including local business owners, experts involved in the construction process, community representatives, and building department employees, we investigate how this technology transition has impacted the work of these different stakeholders. We observe a multifaceted perspective and experience caused by the adoption of OneStop. OneStop exacerbated inequitable practices for local business owners due to a lack of face-to-face interactions with the department employees. For the public sector employees, OneStop standardized the work practices, representing the building department's priorities and values. Based on our findings, we discuss tensions around standardization, equality, and equity in technology transition, as well as design implications for equitable practices in the public sector.

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arXiv2024

The Fall of an Algorithm: Characterizing the Dynamics Toward Abandonment

As more algorithmic systems have come under scrutiny for their potential to inflict societal harms, an increasing number of organizations that hold power over harmful algorithms have chosen (or were required under the law) to abandon them. While social movements and calls to abandon harmful algorithms have emerged across application domains, little academic attention has been paid to studying abandonment as a means to mitigate algorithmic harms. In this paper, we take a first step towards conceptualizing "algorithm abandonment" as an organization's decision to stop designing, developing, or using an algorithmic system due to its (potential) harms. We conduct a thematic analysis of real-world cases of algorithm abandonment to characterize the dynamics leading to this outcome. Our analysis of 40 cases reveals that campaigns to abandon an algorithm follow a common process of six iterative phases: discovery, diagnosis, dissemination, dialogue, decision, and death, which we term the "6 D's of abandonment". In addition, we highlight key factors that facilitate (or prohibit) abandonment, which include characteristics of both the technical and social systems that the algorithm is embedded within. We discuss implications for several stakeholders, including proprietors and technologists who have the power to influence an algorithm's (dis)continued use, FAccT researchers, and policymakers.

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arXiv2024

The Future of Research on Social Technologies: CCC Workshop Visioning Report

Social technologies are the systems, interfaces, features, infrastructures, and architectures that allow people to interact with each other online. These technologies dramatically shape the fabric of our everyday lives, from the information we consume to the people we interact with to the foundations of our culture and politics. While the benefits of social technologies are well documented, the harms, too, have cast a long shadow. To address widespread problems like harassment, disinformation, information access, and mental health concerns, we need to rethink the foundations of how social technologies are designed, sustained, and governed. This report is based on discussions at the Computing Community Consortium Workshop, The Future of Research on Social Technologies, that was held November 2-3, 2023 in Washington, DC. The visioning workshop came together to focus on two questions. What should we know about social technologies, and what is needed to get there? The workshop brought together over 50 information and computer scientists, social scientists, communication and journalism scholars, and policy experts. We used a discussion format, with one day of guiding topics and a second day using an unconference model where participants created discussion topics. The interdisciplinary group of attendees discussed gaps in existing scholarship and the methods, resources, access, and collective effort needed to address those gaps. We also discussed approaches for translating scholarship for various audiences including citizens, funders, educators, industry professionals, and policymakers. This report presents a synthesis of major themes during our discussions. The themes presented are not a summary of what we know already, they are an exploration of what we do not know enough about, and what we should spend more effort and investment on in the coming years.

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arXiv2024

Breaking Political Filter Bubbles via Social Comparison

Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users' reading behavior by motivating them to read viewpoints from the opposing political party.

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arXiv2023

The Public Algorithms Survey in Allegheny County

This survey study focuses on public opinion regarding the use of algorithmic decision-making in government sectors, specifically in Allegheny County, Pennsylvania. Algorithms are becoming increasingly prevalent in various public domains, including both routine and high-stakes government functions. Despite their growing use, public sentiment remains divided, with concerns about privacy and accuracy juxtaposed against perceptions of fairness when compared to human decision-making. In April 2021, a survey was conducted among nearly 1,500 county residents to explore their awareness, experiences, and attitudes towards these algorithms. The study highlights diverse viewpoints influenced by factors such as race, age, education, gender, income, and urban or suburban living. The results demonstrate the complexity of public sentiment towards algorithmic governance and emphasize the need for a nuanced understanding and approach in policy and implementation.

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arXiv2023

Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice

An emerging body of research indicates that ineffective cross-functional collaboration -- the interdisciplinary work done by industry practitioners across roles -- represents a major barrier to addressing issues of fairness in AI design and development. In this research, we sought to better understand practitioners' current practices and tactics to enact cross-functional collaboration for AI fairness, in order to identify opportunities to support more effective collaboration. We conducted a series of interviews and design workshops with 23 industry practitioners spanning various roles from 17 companies. We found that practitioners engaged in bridging work to overcome frictions in understanding, contextualization, and evaluation around AI fairness across roles. In addition, in organizational contexts with a lack of resources and incentives for fairness work, practitioners often piggybacked on existing requirements (e.g., for privacy assessments) and AI development norms (e.g., the use of quantitative evaluation metrics), although they worry that these tactics may be fundamentally compromised. Finally, we draw attention to the invisible labor that practitioners take on as part of this bridging and piggybacking work to enact interdisciplinary collaboration for fairness. We close by discussing opportunities for both FAccT researchers and AI practitioners to better support cross-functional collaboration for fairness in the design and development of AI systems.

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arXiv2023

Towards "Anytime, Anywhere" Community Learning and Engagement around the Design of Public Sector AI

Data-driven algorithmic and AI systems are increasingly being deployed to automate or augment decision processes across a wide range of public service settings. Yet community members are often unaware of the presence, operation, and impacts of these systems on their lives. With the shift towards algorithmic decision-making in public services, technology developers increasingly assume the role of de-facto policymakers, and opportunities for democratic participation are foreclosed. In this position paper, we articulate an early vision around the design of ubiquitous infrastructure for public learning and engagement around civic AI technologies. Building on this vision, we provide a list of questions that we hope can prompt stimulating conversations among the HCI community.

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arXiv2023

Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?

Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives. Researchers have started to develop theoretical and empirical understandings of these user driven audits, with a hope to harness the power of users in detecting harmful machine behaviors. However, little is known about user participation and their division of labor in these audits, which are essential to support these collective efforts in the future. Through collecting and analyzing 17,984 tweets from four recent cases of user driven audits, we shed light on patterns of user participation and engagement, especially with the top contributors in each case. We also identified the various roles user generated content played in these audits, including hypothesizing, data collection, amplification, contextualization, and escalation. We discuss implications for designing tools to support user driven audits and users who labor to raise awareness of algorithm bias.

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