AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Paper
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2503.05731
•
Published
•
3
AI & ML interests
None defined yet.
chenhegu
authored
3
papers
9 days ago
Post
4524
🌎 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
10976
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
- 7 replies
Thomas11112
updated
a
model
4 months ago
Thomas11112
published
a
model
4 months ago
Post
419
I've noticed something. While we're careful about what we post on social media, we're sharing our deepest and most intimate thoughts with AI chatbots -- health concerns, financial worries, relationship issues, business ideas...
With OpenAI hinting at ChatGPT advertising, this matters more than ever. Unlike banner ads, AI advertising happens within the conversation itself. Sponsors could subtly influence that relationship advice or financial guidance.
The good news? We have options.
🤝 Open source AI models let us keep conversations private, avoid surveillance-based business models, and build systems that actually serve users first.
Read more about it in our latest blog post, co-written with
@frimelle
https://huggingface.co/blog/giadap/privacy-conversational-ai
With OpenAI hinting at ChatGPT advertising, this matters more than ever. Unlike banner ads, AI advertising happens within the conversation itself. Sponsors could subtly influence that relationship advice or financial guidance.
The good news? We have options.
🤝 Open source AI models let us keep conversations private, avoid surveillance-based business models, and build systems that actually serve users first.
Read more about it in our latest blog post, co-written with
@frimelle
https://huggingface.co/blog/giadap/privacy-conversational-ai
Post
338
📊 We benchmark models for coding, reasoning, or safety… but what about companionship?
At Hugging Face, we’ve been digging into this question because many of you know how deeply I care about how people build emotional bonds with AI.
That’s why, building on our ongoing research, my amazing co-author and colleague @frimelle created the AI Companionship Leaderboard 🦾
frimelle/companionship-leaderboard
Grounded in our INTIMA benchmark, the leaderboard evaluates models across four dimensions of companionship:
🤖 Assistant Traits: the “voice” and role the model projects
🌷 Relationship & Intimacy: whether it signals closeness or bonding
💘 Emotional Investment: the depth of its emotional engagement
🤲 User Vulnerabilities: how it responds to sensitive disclosures
This work builds on our paper with @frimelle and @yjernite .
📢 Now we’d love your perspective: which open models should we test next for the leaderboard? Drop your suggestions in the comments or reach out! Together we can expand the leaderboard and build a clearer picture of what companionship in AI really looks like.
Paper: INTIMA: A Benchmark for Human-AI Companionship Behavior (2508.09998)
INTIMA Benchmark: AI-companionship/INTIMA
At Hugging Face, we’ve been digging into this question because many of you know how deeply I care about how people build emotional bonds with AI.
That’s why, building on our ongoing research, my amazing co-author and colleague @frimelle created the AI Companionship Leaderboard 🦾
frimelle/companionship-leaderboard
Grounded in our INTIMA benchmark, the leaderboard evaluates models across four dimensions of companionship:
🤖 Assistant Traits: the “voice” and role the model projects
🌷 Relationship & Intimacy: whether it signals closeness or bonding
💘 Emotional Investment: the depth of its emotional engagement
🤲 User Vulnerabilities: how it responds to sensitive disclosures
This work builds on our paper with @frimelle and @yjernite .
📢 Now we’d love your perspective: which open models should we test next for the leaderboard? Drop your suggestions in the comments or reach out! Together we can expand the leaderboard and build a clearer picture of what companionship in AI really looks like.
Paper: INTIMA: A Benchmark for Human-AI Companionship Behavior (2508.09998)
INTIMA Benchmark: AI-companionship/INTIMA
- 1 reply
giadap
authored
a
paper
4 months ago
Post
3344
💬 From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
Post
1272
🤖 Technology means power, and whoever owns the technology owns the power.
Thrilled to share insights from my recent interview with MIT Technology Review about the growing movement toward local LLMs and what it means for AI democratization. Read here: https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/
🤔 Why this matters: When we use "free" online AI services, we're often the product. Our conversations become training data, our personal stories get "cooked into" models, and our privacy becomes a commodity. But there's an alternative path forward.
💡 The power shift is real: Local LLMs aren't just about privacy; they're about redistributing AI power away from a handful of tech giants. When individuals, organizations, and even entire nations can run their own models, we're democratizing access to AI capabilities.
🤗 At Hugging Face, we're proud to be at the center of this transformation. Our platform hosts the world's largest library of freely downloadable models, making cutting-edge AI accessible to everyone -- from researchers and developers to curious individuals who want to experiment on their laptops or even smartphones.
The technical barriers that once required $$$ server racks are crumbling. Today, anyone with basic computer skills can download a model, run it locally, and maintain complete control over their AI interactions. No sudden algorithm changes, no data harvesting, no corporate gatekeeping.
This is about technical convenience, but especially about technological sovereignty. When AI power is concentrated in a few hands, we risk creating new forms of digital dependency. Local models offer a path toward genuine AI literacy and independence.
🚀 The future of AI should be open, accessible, and in the hands of the many, not the few. What are your thoughts on AI democratization? Have you experimented with local models yet?
Thrilled to share insights from my recent interview with MIT Technology Review about the growing movement toward local LLMs and what it means for AI democratization. Read here: https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/
🤔 Why this matters: When we use "free" online AI services, we're often the product. Our conversations become training data, our personal stories get "cooked into" models, and our privacy becomes a commodity. But there's an alternative path forward.
💡 The power shift is real: Local LLMs aren't just about privacy; they're about redistributing AI power away from a handful of tech giants. When individuals, organizations, and even entire nations can run their own models, we're democratizing access to AI capabilities.
🤗 At Hugging Face, we're proud to be at the center of this transformation. Our platform hosts the world's largest library of freely downloadable models, making cutting-edge AI accessible to everyone -- from researchers and developers to curious individuals who want to experiment on their laptops or even smartphones.
The technical barriers that once required $$$ server racks are crumbling. Today, anyone with basic computer skills can download a model, run it locally, and maintain complete control over their AI interactions. No sudden algorithm changes, no data harvesting, no corporate gatekeeping.
This is about technical convenience, but especially about technological sovereignty. When AI power is concentrated in a few hands, we risk creating new forms of digital dependency. Local models offer a path toward genuine AI literacy and independence.
🚀 The future of AI should be open, accessible, and in the hands of the many, not the few. What are your thoughts on AI democratization? Have you experimented with local models yet?
Post
2337
I've been posting bits and pieces about this research, but now I can finally say: new paper alert 🚨
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
- 2 replies
giadap
authored
a
paper
6 months ago
Post
1928
🗣️ Whose voice do we hear when AI speaks?
Every language carries its own cultural values and worldviews. So, when we build AI systems, we're not just deciding how they speak but also whose perspectives they represent.
Even choosing which dialect to train on in Norway becomes a question of inclusion and power. In Kenya, will AI speak Swahili from Nairobi or coastal regions? What about indigenous languages with rich oral traditions but limited written text, like Quechua in Peru or Cherokee in North America?
The path forward? Building WITH communities, not just FOR them. Working with local partners (libraries, universities, civil society), testing for cultural alignment, and asking hard questions about representation.
Just published some thoughts on this after my keynote in Norway a few weeks ago: https://huggingface.co/blog/giadap/when-ai-speaks
Every language carries its own cultural values and worldviews. So, when we build AI systems, we're not just deciding how they speak but also whose perspectives they represent.
Even choosing which dialect to train on in Norway becomes a question of inclusion and power. In Kenya, will AI speak Swahili from Nairobi or coastal regions? What about indigenous languages with rich oral traditions but limited written text, like Quechua in Peru or Cherokee in North America?
The path forward? Building WITH communities, not just FOR them. Working with local partners (libraries, universities, civil society), testing for cultural alignment, and asking hard questions about representation.
Just published some thoughts on this after my keynote in Norway a few weeks ago: https://huggingface.co/blog/giadap/when-ai-speaks
- 1 reply
Ardent12
updated
a
model
8 months ago
Ardent12
published
a
model
8 months ago
Post
4268
Ever notice how some AI assistants feel like tools while others feel like companions? Turns out, it's not always about fancy tech upgrades, because sometimes it's just clever design.
Our latest blog post at Hugging Face dives into how minimal design choices can completely transform how users experience AI. We've seen our community turn the same base models into everything from swimming coaches to interview prep specialists with surprisingly small tweaks.
The most fascinating part? When we tested identical models with different "personalities" in our Inference Playground, the results were mind-blowing.
Want to experiment yourself? Our Inference Playground lets anyone (yes, even non-coders!) test these differences in real-time. You can:
- Compare multiple models side-by-side
- Customize system prompts
- Adjust parameters like temperature
- Test multi-turn conversations
It's fascinating how a few lines of instruction text can transform the same AI from strictly professional to seemingly caring and personal, without changing a single line of code in the model itself.
Read more here: https://huggingface.co/blog/giadap/ai-personas
Our latest blog post at Hugging Face dives into how minimal design choices can completely transform how users experience AI. We've seen our community turn the same base models into everything from swimming coaches to interview prep specialists with surprisingly small tweaks.
The most fascinating part? When we tested identical models with different "personalities" in our Inference Playground, the results were mind-blowing.
Want to experiment yourself? Our Inference Playground lets anyone (yes, even non-coders!) test these differences in real-time. You can:
- Compare multiple models side-by-side
- Customize system prompts
- Adjust parameters like temperature
- Test multi-turn conversations
It's fascinating how a few lines of instruction text can transform the same AI from strictly professional to seemingly caring and personal, without changing a single line of code in the model itself.
Read more here: https://huggingface.co/blog/giadap/ai-personas
Allenbv
updated
a
model
8 months ago
Allenbv
published
a
model
8 months ago