Enterprise AI Analysis
The HCI GenAI CO2ST Calculator: Calculating and Offsetting the Carbon Footprint of Generative Al Use in Human-Computer Interaction Research
This analysis details the HCI GenAI CO2ST Calculator, an innovative tool designed to measure and visualize the carbon footprint of generative AI use in Human-Computer Interaction (HCI) research. Facing a sustainability crisis driven by the high energy consumption of AI models, the calculator provides a crucial step towards transparency and responsible AI development within the HCI community.
Executive Impact: Quantifying GenAI's Environmental Footprint in HCI
The HCI GenAI CO2ST Calculator provides researchers with clear, actionable metrics to understand and mitigate their generative AI usage's environmental cost. By making these hidden impacts visible, the tool fosters a culture of sustainable computing.
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Estimated CO2e for Example HCI Use Case
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Equivalent Car Driving Distance
0 seedlings
Offsetting Tree Seedlings Needed (10yrs)
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Unaccounted Cloud Energy Costs (typical)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Sustainable Computing
AI Research Impact
Methodology & Tool Design
The Imperative for Sustainable HCI
The paper highlights a growing sustainability crisis in computing due to the excessive power consumption of generative AI models. It emphasizes that the responsible audit of tools, like GenAI, is crucial for interaction design, echoing Blevis's concept of Sustainable Interaction Design (SID). The calculator directly addresses the challenge of making these hidden environmental costs transparent.
While efforts exist in the broader AI/ML community for energy-efficient architectures and training, the inference stage, which HCI researchers primarily engage with, still contributes significantly to the carbon footprint. Multi-purpose models, commonly used in HCI, are orders of magnitude more expensive than task-specific systems, exacerbating the problem.
Unveiling GenAI's Carbon Footprint in HCI Research
Generative AI models extensively used in HCI research contribute substantially to carbon emissions. The challenge lies in the opacity of cloud-based services, where hardware and energy consumption are obscured. The HCI GenAI CO2ST Calculator fills this gap by providing a targeted estimation tool for HCI researchers.
Key findings reveal that GenAI energy consumption scales almost linearly with task load; longer prompts, higher resolution images, and extensive dataset generation are particularly carbon-intensive. The calculator aims to foster critical thinking about the necessity and impact of GenAI use, encouraging researchers to adopt mitigation strategies.
A User-Centric Calculator for HCI
The HCI GenAI CO2ST Calculator is designed to be intuitive, allowing researchers to input their GenAI usage details (research phase, model type, usage numbers, input/output resolution). Its back-end utilizes pre-measured energy consumption for various models (e.g., Llama-3.1-Instruct for text-to-text, Stable-diffusion-XL for text-to-image) and converts this to kgCO2e using a global average carbon intensity.
The tool translates complex technical factors into an HCI-friendly interface, providing CO2e estimates alongside relatable equivalents (car distance, flight minutes, tree seedlings). This approach not only provides transparent documentation but also encourages reflection and potential redesign of research experiments to reduce environmental impact. The physical exhibition further enhances awareness by allowing users to 'offset' their footprint with tree seeds.
3.27 kgCO2e
Estimated CO2e for a Typical HCI GenAI Use Case
Enterprise Process Flow: GenAI Integration in HCI Research Phases
Research Planning
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Prototyping & Building
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Evaluation & User Studies
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Data Collection
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Analysis & Synthesis
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Dissemination & Communication
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AI Model Training or Fine-tuning
| Feature | HCI GenAI CO2st Calculator | General AI Carbon Trackers |
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| Focus | Specifically designed for HCI research pipelines | Broad ML/AI model development and training |
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Case Study: The Interactive Tree Exhibition – Embodied Carbon Footprint
The HCI GenAI CO2ST Calculator features a unique physical exhibition that brings the abstract concept of carbon footprint to life. A large cardboard tree with glowing leaves serves as an interactive display. Users input their GenAI research usage and receive a CO2e estimate, which is then printed on a receipt.
These receipts are hung on the tree, creating a visual 'body of evidence' for GenAI's carbon impact. Crucially, users can exchange their CO2e for native tree seeds, enabling a direct act of offsetting. This embodied interaction design not only raises awareness but provides a tangible mechanism for engaging with sustainability in HCI research.
Estimate Your Enterprise AI Sustainability ROI
While this paper focuses on research, the principles of measuring and mitigating AI's environmental impact are critical for enterprise adoption. Use our generalized calculator to estimate potential efficiency gains and cost savings from optimized AI implementation.
Estimated Annual Savings
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Strategic Roadmap: Fostering Sustainable GenAI Practices
Implementing the insights from the HCI GenAI CO2ST Calculator within an enterprise requires a phased approach, moving from initial awareness to proactive mitigation and transparent reporting.
Phase 1: Carbon Footprint Awareness
Utilize tools like the HCI GenAI CO2ST Calculator to understand the environmental cost of current GenAI applications in research and development. Identify key areas of high energy consumption, such as large-scale data generation or high-resolution outputs.
Phase 2: Transparency & Documentation
Establish internal guidelines for transparently documenting the carbon footprint of GenAI use in all research reports and project summaries. Advocate for clearer data from cloud service providers regarding energy consumption of their AI models.
Phase 3: Mitigation Strategy Development
Develop and implement strategies to reduce GenAI's carbon footprint. This includes refining prompting techniques, setting user limits, prioritizing task-specific models over general-purpose ones, and exploring lower-energy alternatives for certain tasks.
Phase 4: Ecosystem Engagement & Offsetting
Engage with the wider AI community and internal stakeholders to promote sustainable AI practices. Explore and implement certified carbon offsetting initiatives to compensate for unavoidable emissions, mirroring the calculator's tree-seed exchange.
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