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

arXiv: https://arxiv.org/abs/2204.13842 · HTML: https://mlsanity.com/paper/2204.13842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 90 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop](https://mlsanity.com/paper/1703.08428/) (2017)
- [Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations](https://mlsanity.com/paper/1909.00031/) (2019)
- [Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors](https://mlsanity.com/paper/2006.06820/) (2020)
- [Not at Home on the Range: Peer Production and the Urban/Rural Divide](https://mlsanity.com/paper/1908.10954/) (2019)
- [Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations](https://mlsanity.com/paper/1909.10005/) (2019)
- [Geno: A Developer Tool for Authoring Multimodal Interaction on Existing Web Applications](https://mlsanity.com/paper/2007.09809/) (2020)

## Built on by (most cited first, 1 of 1)

- ["Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work](https://mlsanity.com/paper/2603.07459/) (2026)

## 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.