Alexander Boltz

schoolUniversity of Texas at Austinbar_chart#1823 Global Rank

Total Papers

HCI Rank

#1823

Research Areas

Years Active

Recent Publications

Beyond Accuracy: Experts See AI Fact-Checks as Accurate but Less Useful

As misinformation proliferates online, large language models (LLMs) have been proposed as a promising tool to accelerate fact-checking workflows. While LLMs demonstrate strong performance in tasks such as text annotation, their capabilities in generating fact-checking reports remain uncertain. To investigate how media…

CSCW '243rd AuthorHonorable MentionSession 4d: Misinformation and Fact Checking

Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI

While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such NLP work due to poor alignment with fact-checker practices, values, and needs. To address this, we investigate a co-design method, Matchma…

CHI '243rd AuthorPrivacy by Design & User Control +1

Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work

Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated worker…

CHI '232nd AuthorExplainable AI (XAI) +2

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of…

CSCW '222nd AuthorPolitics, Polarization and Partisanship; Politics, Polarization and Partisanship

Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation

Hyper-partisan misinformation has become a major public concern. To examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social media, we conducted a 4 (label type: algorithm, community, third-party fact-checker, and no label) X 2 (post ideology: liberal and conservative) bet…

CHI '222nd AuthorHonorable MentionCrowdsourcing Task Design & Quality Control +1

Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work

Prior research has studied the detrimental impact of algorithmic management on gig workers and strategies that workers devise in response. However, little work has investigated alternative platform designs to promote worker well-being, particularly from workers' own perspectives. We use a participatory design approach…

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