Reconsidering CO2 emissions from Computer Vision
arXiv:2104.08702
Abstract
Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the Computer Vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.
arXiv commentAccepted for publication in CVPR 2021 Workshop
References in corpus (12)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Artificial Intelligence: the global landscape of ethics guidelines
- DARTS: Differentiable Architecture Search
- Neural Architecture Optimization
- One-Shot Neural Architecture Search via Self-Evaluated Template Network
- Neural Architecture Transfer
- Network Pruning via Transformable Architecture Search
- Noisy Differentiable Architecture Search
- sharpDARTS: Faster and More Accurate Differentiable Architecture Search
- Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
- AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
- DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search