Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts

In episode 120 of The Gradient Podcast, Daniel Bashir (twitter.com/spaniel_bashir) speaks to Sasha Luccioni.
Sasha is the AI and Climate Lead at HuggingFace, where she spearheads research, consulting, and capacity-building to elevate the sustainability of AI systems. A founding member of Climate Change AI (CCAI) (climatechange.ai) and a board member of Women in Machine Learning (WiML) (sashaluccioni.com/about/wimlworkshop.org), Sasha is passionate about catalyzing impactful change, organizing events and serving as a mentor to under-represented minorities within the AI community.
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Outline:
* (00:00) Intro
* (00:43) Sasha’s background
* (01:52) How Sasha became interested in sociotechnical work
* (03:08) Larger models and theory of change for AI/climate work
* (07:18) Quantifying emissions for ML systems
* (09:40) Aggregate inference vs training costs
* (10:22) Hardware and data center locations
* (15:10) More efficient hardware vs. bigger models — Jevons paradox
* (17:55) Uninformative experiments, takeaways for individual scientists, knowledge sharing, failure reports
* (27:10) Power Hungry Processing: systematic comparisons of ongoing inference costs
* (28:22) General vs. task-specific models
* (31:20) Architectures and efficiency
* (33:45) Sequence-to-sequence architectures vs. decoder-only
* (36:35) Hardware efficiency/utilization
* (37:52) Estimating the carbon footprint of Bloom and lifecycle assessment
* (40:50) Stable Bias
* (46:45) Understanding model biases and representations
* (52:07) Future work
* (53:45) Metaethical perspectives on benchmarking for AI ethics
* (54:30) “Moral benchmarks”
* (56:50) Reflecting on “ethicality” of systems
* (59:00) Transparency and ethics
* (1:00:05) Advice for picking research directions
* (1:02:58) Outro
Links:
* Sasha’s homepage and Twitter
* Papers read/discussed
* Climate Change / Carbon Emissions of AI Models
* Quantifying the Carbon Emissions of Machine Learning (arxiv.org/abs/1910.09700)
* Power Hungry Processing: Watts Driving the Cost of AI Deployment? (arxiv.org/pdf/2311.16863.pdf)
* Tackling Climate Change with Machine Learning (dl.acm.org/doi/pdf/10.1145/3485128)
* CodeCarbon (codecarbon.io)
* Responsible AI
* Stable Bias: Analyzing Societal Representations in Diffusion Models (arxiv.org/abs/2303.11408)
* Metaethical Perspectives on ‘Benchmarking’ AI Ethics (arxiv.org/pdf/2204.05151.pdf)
* Measuring Data (arxiv.org/pdf/2212.05129.pdf)
* Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice (arxiv.org/pdf/2308.07120.pdf)

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