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AI, Jobs, and the Automation Trap: Where Is HCI?

Marios Constantinides, Daniele Quercia

TL;DR

The paper critiques the automation-centric trajectory of AI in the workplace and argues that Human-Centered AI (HCAI) remains underrepresented in patents and real-world deployments. By analyzing AI patent trends and healthcare applications, it shows that automation dominates, while augmentation is underexplored due to factors like translational gaps and short-term incentives. It then surveys domain-specific examples of augmentation in education, workplace, and healthcare to illustrate viable HCAI pathways. Finally, it offers six actionable recommendations—spanning responsible design, academia-industry collaboration, scalable prototypes, worker upskilling, feedback mechanisms, and policy engagement—to translate HCAI into tangible, real-world impact.

Abstract

As artificial intelligence (AI) continues to reshape the workforce, its current trajectory raises pressing questions about its ultimate purpose. Why does job automation dominate the agenda, even at the expense of human agency and equity? This paper critiques the automation-centric paradigm, arguing that current reward structures, which largely focus on cost reduction, drive the overwhelming emphasis on task replacement in AI patents. Meanwhile, Human-Centered AI (HCAI), which envisions AI as a collaborator augmenting human capabilities and aligning with societal values, remains a fugitive from the mainstream narrative. Despite its promise, HCAI has gone ``missing'', with little evidence of its principles translating into patents or real-world impact. To increase impact, actionable interventions are needed to disrupt existing incentive structures within the HCI community. We call for a shift in priorities to support translational research, foster cross-disciplinary collaboration, and promote metrics that reward tangible and real-world impact.

AI, Jobs, and the Automation Trap: Where Is HCI?

TL;DR

The paper critiques the automation-centric trajectory of AI in the workplace and argues that Human-Centered AI (HCAI) remains underrepresented in patents and real-world deployments. By analyzing AI patent trends and healthcare applications, it shows that automation dominates, while augmentation is underexplored due to factors like translational gaps and short-term incentives. It then surveys domain-specific examples of augmentation in education, workplace, and healthcare to illustrate viable HCAI pathways. Finally, it offers six actionable recommendations—spanning responsible design, academia-industry collaboration, scalable prototypes, worker upskilling, feedback mechanisms, and policy engagement—to translate HCAI into tangible, real-world impact.

Abstract

As artificial intelligence (AI) continues to reshape the workforce, its current trajectory raises pressing questions about its ultimate purpose. Why does job automation dominate the agenda, even at the expense of human agency and equity? This paper critiques the automation-centric paradigm, arguing that current reward structures, which largely focus on cost reduction, drive the overwhelming emphasis on task replacement in AI patents. Meanwhile, Human-Centered AI (HCAI), which envisions AI as a collaborator augmenting human capabilities and aligning with societal values, remains a fugitive from the mainstream narrative. Despite its promise, HCAI has gone ``missing'', with little evidence of its principles translating into patents or real-world impact. To increase impact, actionable interventions are needed to disrupt existing incentive structures within the HCI community. We call for a shift in priorities to support translational research, foster cross-disciplinary collaboration, and promote metrics that reward tangible and real-world impact.

Paper Structure

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Does Automation Dominate AI’s current Design Ethos?
  3. Automation Dominates
  4. Why Does Automation Dominate?
  5. Any Alternatives Fostering Augmentation?
  6. Augmenting Learning and Development in Education
  7. Augmenting Human Capabilities in the Workplace
  8. Augmenting Decision-Making in Healthcare
  9. How Can HCI Translate into Real-world AI Deployments?
  10. Taking a "Responsible AI by Design" Approach
  11. Fostering Academia-Industry Collaboration for Workforce Innovation
  12. Developing Scalable Prototypes for Workforce Applications
  13. Empowering Workers Through Job Preservation and Skill Development
  14. Embedding Feedback Loops to Address Worker Concerns
  15. Shaping Policy for Augmentative AI
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Jobs impacted by AI in the healthcare sector as visualized in the AI Impact dashboard (https://social-dynamics.net/aii/) based on the methodology described by Septiandri et al. septiandri2024potential. The most impacted jobs are primarily automation-driven, focusing on tasks such as automated vascular analysis and MRI system operations. In contrast, augmentation-driven tasks (e.g., patient-specific therapy planning and dynamic health record management) are less prominent.