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

Upol Ehsan, Samir Passi, Koustuv Saha, Todd McNutt, Mark O. Riedl, Sara Alcorn

TL;DR

This paper reframes the future of AI in work by focusing on workers and introducing the AI-as-Amplifier Paradox, the idea that AI can simultaneously amplify performance and erode expertise. Through a year-long longitudinal study in radiation oncology, it documents asymptomatic effects that quietly de-skill clinicians, progresses to chronic harms, and culminates in identity commoditization. It then presents a Social Transparency intervention and a multilevel Dignified Human-AI Interaction Framework grounded in sociotechnical immunity (Sense-Contain-Recover) to detect, contain, and recover from hidden harms. The framework is co-constructed with workers and demonstrated to transfer across healthcare to software engineering, offering practical mechanisms to balance productivity with preservation of human expertise and dignity. The work advances the field by treating dignity and skill preservation as first-class success metrics in AI-mediated work and by providing actionable design and organizational guidance for resilient, humane automation.

Abstract

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.

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

TL;DR

This paper reframes the future of AI in work by focusing on workers and introducing the AI-as-Amplifier Paradox, the idea that AI can simultaneously amplify performance and erode expertise. Through a year-long longitudinal study in radiation oncology, it documents asymptomatic effects that quietly de-skill clinicians, progresses to chronic harms, and culminates in identity commoditization. It then presents a Social Transparency intervention and a multilevel Dignified Human-AI Interaction Framework grounded in sociotechnical immunity (Sense-Contain-Recover) to detect, contain, and recover from hidden harms. The framework is co-constructed with workers and demonstrated to transfer across healthcare to software engineering, offering practical mechanisms to balance productivity with preservation of human expertise and dignity. The work advances the field by treating dignity and skill preservation as first-class success metrics in AI-mediated work and by providing actionable design and organizational guidance for resilient, humane automation.

Abstract

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.
Paper Structure (48 sections, 5 figures, 2 tables)

This paper contains 48 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Future of Work vs. Future of Workers
  4. Troubling Signs in Human-AI Interaction and Collaboration
  5. AI Harms: Impacts to Human Dignity and Professional Identity
  6. Study Context & Methods
  7. Study Domain: AI in Radiation Oncology
  8. Case Study Background
  9. The Problem and Intervention:
  10. Participants: Overcoming Access Challenges and Building Community Trust
  11. Data Collection & Analysis
  12. Ethics, Privacy, and Reflexivity
  13. Findings: From Asymptomatic Effects to Chronic Harms to Identity Commoditization
  14. Emergence of the AI-as-Amplifier Paradox: From Enhancement to Erosion
  15. Asymptomatic Effects: Hidden Shifts Beneath the Surface
  16. ...and 33 more sections

Figures (5)

  • Figure 1: Anatomy of AI-assisted radiation treatment planning in RadPlan. This reconstructed simplified Dose-Volume Histogram (DVH) shows how AI predictions (yellow band) relate to actual dose delivery, best achievable doses, and protocol targets.
  • Figure 2: Social Transparency intervention: fosters better decisions by complementing AI dose predictions (left) with socio-organizational context (right). Captures the 4W (who, what, when, why) of multi-stakeholder input, revealing how treatment decisions evolve.
  • Figure 3: The AI-as-Amplifier Paradox emerges from participants' depictions of their evolving RadPlan relationship. Panels combine workshop drawings into a participant-guided arc showing initial optimism devolving through asymptomatic effects and chronic harms into identity commoditization. Visuals honor the minimalist aesthetic with participant-requested upscaling. See \ref{['sec:appendix_compactPicsComics']} for a compact version.
  • Figure 4: Trajectory of the AI-as-Amplifier Paradox: conventional metrics capture first-wave optimism and short-term gains but mask the hidden expertise erosion cascade (asymptomatic effects, chronic harms, identity commoditization) that diverges progressively from visible performance indicators
  • Figure 5: Visual narrative of the erosion-to-dignity journey. The AI-as-Amplifier Paradox (left) threatens human expertise. The framework (middle) addresses this by building sociotechnical immunity across Worker-Technology-Organization levels using the Immunity Triad (Sense, Contain, Recover). This process ultimately fosters Dignified Human-AI Interaction (right).