mlsanityData to 7 Oct 2026

A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality

arXiv 2204.13842 ↗First posted 29 Apr 2022cs.HC cs.AIcited by 1

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Abstract (arXiv)

The growing inequality in gig work between workers and platforms has become a critical social issue as gig work plays an increasingly prominent role in the future of work. The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms. In this position paper, we argue that a bottom-up approach that empowers individual workers to access AI-enabled work planning support and share data among a group of workers through a network of end-user-programmable intelligent assistants is a practical way to bridge AI inequality in gig work under the current paradigm of privately owned platforms. This position paper articulates a set of research challenges, potential approaches, and community engagement opportunities, seeking to start a dialogue on this important research topic in the interdisciplinary CHIWORK community.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 90 references that are in the map. First posted on arXiv 29 Apr 2022.

Builds on

7 of 90 references.

References · 7

  1. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  2. 2017Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loopcited by 26
  3. 2019Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrationscited by 26
  4. 2020Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviorscited by 12
  5. 2019Not at Home on the Range: Peer Production and the Urban/Rural Dividecited by 6
  6. 2019Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendationscited by 6
  7. 2020Geno: A Developer Tool for Authoring Multimodal Interaction on Existing Web Applicationscited by 3

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