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Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence

KC Santosh, Rodrigue Rizk, Longwei Wang

TL;DR

The paper argues that the current scale-driven AI paradigm incurs unsustainable environmental and ethical costs. It introduces Human AI (HAI), a lifelong, incremental learning framework guided by meta-learning, active human collaboration, and carbon-aware scheduling to balance accuracy with energy use and annotation effort. By formalizing a constrained, multi-objective optimization problem and outlining a modular architecture, the work provides a concrete path toward responsible, human-centered AI that learns adaptively under ecological limits. The proposed unified benchmarks and governance-oriented design emphasize transparency, explainability, and systems-level sustainability, with potential impact across critical domains requiring agility and low emissions.

Abstract

The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms, advocating for a shift toward human-inspired, sustainable AI solutions. We introduce a novel framework, Human AI (HAI), which emphasizes incremental learning, carbon-aware optimization, and human-in-the-loop collaboration to enhance adaptability, efficiency, and accountability. By drawing parallels with biological cognition and leveraging dynamic architectures, HAI seeks to balance performance with ecological responsibility. We detail the theoretical foundations, system design, and operational principles that enable AI to learn continuously and contextually while minimizing carbon footprints and human annotation costs. Our approach addresses pressing challenges in active learning, continual adaptation, and energy-efficient model deployment, offering a pathway toward responsible, human-centered artificial intelligence.

Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence

TL;DR

The paper argues that the current scale-driven AI paradigm incurs unsustainable environmental and ethical costs. It introduces Human AI (HAI), a lifelong, incremental learning framework guided by meta-learning, active human collaboration, and carbon-aware scheduling to balance accuracy with energy use and annotation effort. By formalizing a constrained, multi-objective optimization problem and outlining a modular architecture, the work provides a concrete path toward responsible, human-centered AI that learns adaptively under ecological limits. The proposed unified benchmarks and governance-oriented design emphasize transparency, explainability, and systems-level sustainability, with potential impact across critical domains requiring agility and low emissions.

Abstract

The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms, advocating for a shift toward human-inspired, sustainable AI solutions. We introduce a novel framework, Human AI (HAI), which emphasizes incremental learning, carbon-aware optimization, and human-in-the-loop collaboration to enhance adaptability, efficiency, and accountability. By drawing parallels with biological cognition and leveraging dynamic architectures, HAI seeks to balance performance with ecological responsibility. We detail the theoretical foundations, system design, and operational principles that enable AI to learn continuously and contextually while minimizing carbon footprints and human annotation costs. Our approach addresses pressing challenges in active learning, continual adaptation, and energy-efficient model deployment, offering a pathway toward responsible, human-centered artificial intelligence.

Paper Structure

This paper contains 27 sections, 9 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Green AI and the Carbon Footprint Associated With Machine Learning Models
  3. The Myth of Big Data and the Cost of Waiting
  4. Learning Every Day: A Human Model for Machines
  5. Meta-Learning and Data Efficiency
  6. Human-in-the-Loop as Governance Mechanism
  7. Toward Human-Centered Trustworthy AI: Unifying Active Learning and Explainability
  8. Human-in-the-Loop Trust through Active Learning
  9. Explainability Beyond Visual Plausibility
  10. The Cognitive Shift: From Static to Lifelong AI
  11. Where Computational Power Should Matter
  12. The Path Forward: Responsible Human-Like AI
  13. Toward Adaptive and Energy-Efficient Artificial Neural Network Architectures
  14. Problem Formulation
  15. Task-Based Formulation
  16. ...and 12 more sections

Figures (3)

  • Figure 1: Humans selectively engage a relevant subset of neurons based on the nature and complexity of the input.
  • Figure 2: Illustration of task-specific resource allocation using a tool analogy. Just as a shovel is suited for moving snow and a spoon for adding sugar to coffee or tea, neural systems should engage distinct subsets of neurons depending on the nature and complexity of the input. This analogy underscores the importance of selective activation in efficient cognitive processing.
  • Figure 3: Human AI (HAI) modular architecture, comprising Meta-Learning Core ($\mathcal{M}$), Active Data Selector ($\mathcal{A}$), Carbon-Aware Scheduler ($\mathcal{C}$), Human Feedback Interface $(\mathcal{H})$, and Continual Memory $(\mathcal{R})$. Arrows denote data and control flow across modules under energy and annotation constraints.