Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Paper • 2606.09809 • Published • 4
AI & ML interests
datasets, social impact, bias, evaluation
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evijit
authored a
paper about 9 hours ago
Post
1056
Weekend mini project! Since commentary on AI is inherently interdisciplinary, we connected the observations in the Pope's encyclical with decades of scholarship in Responsible AI and Ethics research and created an interactive space with these annotations!
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
jjzha
authored 4
papers 15 days ago
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
Paper • 2601.18026 • Published
UniSkill: A Dataset for Matching University Curricula to Professional Competencies
Paper • 2603.03134 • Published
WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain
Paper • 2604.13055 • Published
CroCo: Cross-Lingual Contrastive Preference Tuning on Self-Generations
Paper • 2605.26293 • Published • 6
jjzha
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evijit
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KaiserWhoLearns
authored 2
papers about 2 months ago
KaiserWhoLearns
submitted a
paper to Daily Papers about 2 months ago
meg
updated a
dataset 2 months ago
amupd
authored a
paper 3 months ago
KaiserWhoLearns
authored a
paper 3 months ago
KaiserWhoLearns
submitted a
paper to Daily Papers 3 months ago
KaiserWhoLearns
authored a
paper 4 months ago
yjernite
authored a
paper 5 months ago
Post
4353
🤖 Did you know your voice might be cloned without your consent from just *one sentence* of audio?
That's not great. So with @frimelle , we brainstormed a new idea for developers who want to curb malicious use: ✨The Voice Consent Gate.✨
Details, code, here: https://huggingface.co/blog/voice-consent-gate
That's not great. So with @frimelle , we brainstormed a new idea for developers who want to curb malicious use: ✨The Voice Consent Gate.✨
Details, code, here: https://huggingface.co/blog/voice-consent-gate
- 3 replies
Post
4653
🌎 AI ethics and sustainability are two sides of the same coin.
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
- 1 reply
Post
2886
AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
Discord: https://discord.com/invite/VYkdEVjJ5J