mlsanityData to 14 Oct 2026

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

arXiv 2601.21920 ↗First posted 29 Jan 2026cs.HC cs.AI cs.CYcited by 6

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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.

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

18 of 112 references.

References · 18

  1. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
  2. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  3. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  4. 2020A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scorescited by 74
  5. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 64
  6. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  7. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  8. 2024Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactionscited by 20
  9. 2023Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholderscited by 13
  10. 2024The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilotcited by 13
  11. 2025AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Supportcited by 12
  12. 2024AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworkercited by 11
6 more
  1. 2025De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Designcited by 11
  2. 2024Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplacecited by 7
  3. 2023Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?cited by 6
  4. 2024Addressing Social Misattributions of Large Language Models: An HCXAI-based Approachcited by 3
  5. 2024"It's Always a Losing Game": How Workers Understand and Resist Surveillance Technologies on the Jobcited by 2
  6. 2025The New Calculator? Practices, Norms, and Implications of Generative AI in Higher Educationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.