Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework

Eduardo Di Santi

Abstract

Artificial intelligence is increasingly embedded in human decision-making, where it can either enhance human reasoning or induce excessive cognitive dependence. This paper introduces a conceptual and mathematical framework for distinguishing cognitive amplification, in which AI improves hybrid human-AI performance while preserving human expertise, from cognitive delegation, in which reasoning is progressively outsourced to AI systems. To characterize these regimes, we define a set of operational metrics: the Cognitive Amplification Index (CAI*), the Dependency Ratio (D), the Human Reliance Index (HRI), and the Human Cognitive Drift Rate (HCDR). Together, these quantities provide a low-dimensional metric space for evaluating not only whether human-AI systems achieve genuine synergistic performance, but also whether such performance is cognitively sustainable for the human component over time. The framework highlights a central design tension in human-AI systems: maximizing short-term hybrid capability does not necessarily preserve long-term human cognitive competence. We therefore argue that human-AI systems should be designed under a cognitive sustainability constraint, such that gains in hybrid performance do not come at the cost of degradation in human expertise.

Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework

Abstract

Artificial intelligence is increasingly embedded in human decision-making, where it can either enhance human reasoning or induce excessive cognitive dependence. This paper introduces a conceptual and mathematical framework for distinguishing cognitive amplification, in which AI improves hybrid human-AI performance while preserving human expertise, from cognitive delegation, in which reasoning is progressively outsourced to AI systems. To characterize these regimes, we define a set of operational metrics: the Cognitive Amplification Index (CAI*), the Dependency Ratio (D), the Human Reliance Index (HRI), and the Human Cognitive Drift Rate (HCDR). Together, these quantities provide a low-dimensional metric space for evaluating not only whether human-AI systems achieve genuine synergistic performance, but also whether such performance is cognitively sustainable for the human component over time. The framework highlights a central design tension in human-AI systems: maximizing short-term hybrid capability does not necessarily preserve long-term human cognitive competence. We therefore argue that human-AI systems should be designed under a cognitive sustainability constraint, such that gains in hybrid performance do not come at the cost of degradation in human expertise.
Paper Structure (15 sections, 23 equations, 2 figures)

This paper contains 15 sections, 23 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. Cognitive Amplification
  5. Cognitive Amplification Index
  6. Dependency Ratio
  7. Human Cognitive Drift
  8. Regimes of Human--AI Collaboration
  9. Design Implications
  10. Illustrative Measurement of $Q$
  11. Illustrative Examples of the Framework
  12. Pharmaceutical Safety Monitoring.
  13. Industrial Anomaly Diagnosis.
  14. Discussion
  15. Conclusion

Figures (2)

  • Figure 1: Two regimes of human--AI interaction. Left: cognitive amplification emerges when AI supports an active human cognitive loop. Right: cognitive delegation arises when reasoning is progressively outsourced to AI, increasing dependency and reducing human cognitive engagement over time.
  • Figure 2: Conceptual phase diagram of human--AI collaboration regimes. The horizontal axis represents the Dependency Ratio $D$, measuring the degree of AI dominance in the hybrid system. The vertical axis represents the Cognitive Amplification Index $CAI^*$, indicating whether the human--AI system achieves genuine synergy over the best standalone component. Human Cognitive Drift ($HCDR$) determines whether a regime preserves or degrades long-term human expertise.