ethics
awesome-ai-safety
ethics | awesome-ai-safety | |
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1 | 6 | |
265 | 166 | |
0.0% | 0.0% | |
0.0 | 5.6 | |
almost 2 years ago | over 1 year ago | |
Python | ||
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ethics
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[P] Request: Any datasets of morality stories?
Code for https://arxiv.org/abs/2008.02275 found: https://github.com/hendrycks/ethics
awesome-ai-safety
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Ask HN: Who is hiring? (March 2025)
Giskard - Testing platform for LLM Agents | Multiple roles | Full-time | France | https://giskard.ai/
We're looking for early-stage talent to join our team in these roles:
1. Frontend Design Engineer: https://apply.workable.com/giskard/j/EB872047FF/
2. AI Security & Safety Researcher: https://apply.workable.com/giskard/j/E89FE8E310/
3. LLM Developer Advocate: https://apply.workable.com/giskard/j/FD207CDFBD/
4. Customer Success Manager: https://apply.workable.com/giskard/j/1446F6A1B6/
5. Enterprise Account Executive: https://apply.workable.com/giskard/j/1F95E78192/
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Ask HN: Who is hiring? (October 2023)
Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/
We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.
Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Quality & Safety.
Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.
In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022
In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...
We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!
We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:
- Ask HN: Who is hiring? (August 2023)
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Show HN: Python library to scan ML models for vulnerabilities
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.
It is still a beta and I would love to hear your feedback if you have the time to try it out.
We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.
If you are interested in the code:
https://github.com/Giskard-AI/giskard/tree/main/python-clien...
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[R] Awesome AI Safety – A curated list of papers & technical articles on AI Quality & Safety
Repository: https://github.com/Giskard-AI/awesome-ai-safety
- AI Safety – curated papers for safer, ethical, and reliable AI
What are some alternatives?
ToolEmu - [ICLR'24 Spotlight] A language model (LM)-based emulation framework for identifying the risks of LM agents with tool use
giskard - 🐢 Open-Source Evaluation & Testing for AI & LLM systems
natural-adv-examples - A Harder ImageNet Test Set (CVPR 2021)
opentofu - OpenTofu lets you declaratively manage your cloud infrastructure.
moonwatcher - Evaluation & testing framework for computer vision models
awesome-open-data-annotation - Open Source Data Annotation & Labeling Tools