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20232026
most citedWikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia

19 citations · 46 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.HC2025

Understanding, Challenging, and Demystifying Perceptions of Gig Worker Vulnerabilities

Sander de Jong, Jane Hsieh, Tzu-Sheng Kuo +3

Across service domains, platform-based gig workers often face a wide range of severe yet hidden vulnerabilities, including opaque pay practices, illusions of flexibility, health an…

cs.HC2025★ 9 cited

Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work

Jane Hsieh, Angie Zhang, Sajel Surati +6

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are expos…

cs.HC2024

The Future of Open Human Feedback

Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo +17

Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desir…

cs.HC2024★ 12 cited

PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation

Tzu-Sheng Kuo, Quan Ze Chen, Amy X. Zhang +3

Community and organizational policies are typically designed in a top-down, centralized fashion, with limited input from impacted stakeholders. This can result in policies that are…

cs.HC2024★ 19 cited

Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia

Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng +5

AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which…