Track emissions from Compute and recommend ways to reduce their impact on the environment.
This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
🌟 A curated collection of free, high quality AI tools 🤖, APIs 🔗, datasets 📊, and learning resources 📚 covering machine learning 🧠, deep learning 🧩, generative AI 🎨, NLP 💬, and data science 📈. Designed to help developers 👩💻, researchers 🔬, and creators ✨ explore and build with AI faster ⚡.
Free and open source code of the https://tournesol.app platform. Meet the community on Discord https://discord.gg/WvcSG55Bf3
This AI fact-checking system, built with LangGraph, dissects text into verifiable claims, cross-referencing them with real-world evidence via web searches. It then generates detailed accuracy reports, ideal for combating misinformation in LLM outputs, news, or any text.
Courses on Kaggle
List of references about Machine Learning bias and ethics
AI makes users productive — but also cognitively lazy. Lucid detects cognitive dependency in real-time and adapts AI responses to keep users thinking. Drop-in SDK: zero latency, any LLM provider, research-backed.
开源劳动者AI研究与评测项目：建设劳动知识、公开评测基准与共同治理机制｜Open worker-centered AI research and evaluation project.
🛡️ A curated list of tools, frameworks, standards, and resources for AI agent governance, safety, and compliance
A long-form essay exploring the philosophy of minimalist AI, how future intelligent systems can be calm, ethical, and invisible. Inspired by calm technology, design minimalism, and cognitive science, Quiet Machines envisions a world where the best technology listens more than it speaks.
An in-depth exploration of the rise of human-centered, interactive machine learning. This article examines how Streamlit enables collaborative AI design by merging UX, visualization, and automation. Includes theory, architecture, and design insights from the ML Playground project.
BMAD AI/ML Engineering Expansion Pack - Streamlined framework for AI Singapore programs (MVP, POC, SIP, LADP) with specialized agents, workflows, and templates for ML/LLM development
A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.
A beginner-friendly AI Governance & Risk Toolkit — risk register, governance templates, and audit-ready workflows for early-stage AI teams.
A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.
A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest “twin” examples, neighborhood tightness, mixed-vs-homogeneous evidence, and “no reliable twins” abstention. Argues similarity and evidence packets beat probability scores for trust and safety.
Planning and collaboration hub for the CHAOSS AI Alignment Working Group. We develop metrics to evaluate how effectively AI systems respect and align with open source community values, expectations, and projects. New contributors and community members welcome!
An Introduction to Transparent Machine Learning
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