# From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction

arXiv: https://arxiv.org/abs/2601.21920 · HTML: https://mlsanity.com/paper/2601.21920/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 112 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions](https://mlsanity.com/paper/2402.11364/) (2024)
- [Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders](https://mlsanity.com/paper/2303.06794/) (2023)
- [The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot](https://mlsanity.com/paper/2409.08379/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker](https://mlsanity.com/paper/2411.02408/) (2024)
- [De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Design](https://mlsanity.com/paper/2503.03924/) (2025)
- [Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace](https://mlsanity.com/paper/2410.18334/) (2024)
- [Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?](https://mlsanity.com/paper/2303.07242/) (2023)
- [Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach](https://mlsanity.com/paper/2403.17873/) (2024)
- ["It's Always a Losing Game": How Workers Understand and Resist Surveillance Technologies on the Job](https://mlsanity.com/paper/2412.06945/) (2024)
- [The New Calculator? Practices, Norms, and Implications of Generative AI in Higher Education](https://mlsanity.com/paper/2501.08864/) (2025)

## Built on by (most cited first, 6 of 6)

- [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026)
- [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026)
- [The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading](https://mlsanity.com/paper/2604.03501/) (2026)
- [From Tool to Agent: How Worker Needs Reorganize Across the Agentic Roles of Workplace AI](https://mlsanity.com/paper/2605.03078/) (2026)
- [Psychological Competence as a Missing Dimension in AI Evaluation](https://mlsanity.com/paper/2607.08285/) (2026)
- [Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work](https://mlsanity.com/paper/2609.29901/) (2026)

## Abstract (arXiv)

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI's dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. Initial operational gains hid ``intuition rust'': the gradual dulling of expert judgment. These asymptomatic effects evolved into chronic harms, such as skill atrophy and identity commoditization. Building on these findings, we offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections. The framework operationalizes sociotechnical immunity through dual-purpose mechanisms that serve institutional quality goals while building worker power to detect, contain, and recover from skill erosion, and preserve human identity. Evaluated across healthcare and software engineering, our work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with the preservation of human expertise.