#Responsible-ai
Showing 42 of 42 repositories tagged #responsible-ai, ranked by stars
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
🐢 Open-Source Evaluation & Testing library for LLM Agents
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
A Python package to assess and improve fairness of machine learning models.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
moDel Agnostic Language for Exploration and eXplanation
Catch your AI's mistakes and blind spots before your customers or regulators do. iFixAi runs 45 inspections, 32 graded core plus 13 extended for frontier risks like sabotage, sandbagging, and oversight evasion. It returns a letter grade in under 5 minutes. Industry and model agnostic.
RAG Time: A 5-week Learning Journey to Mastering RAG
A resource repository for machine unlearning in large language models
Deliver safe & effective language models
The testing platform for AI teams. Bring engineers, PMs, and domain experts together to generate tests, simulate (adversarial) conversations, and trace every failure to its root cause.
The IQ Series is a hands-on learning experience for Microsoft IQ: Microsoft's unified intelligence layer for the enterprise, spanning Foundry IQ, Work IQ, and Fabric IQ. The series includes video episodes, Jupyter notebooks, and Azure deployment templates.
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments
A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.
Carefully curated list of awesome data science resources.
Alignment-research scaffold (autoresearch-style) for LLM guardrails: search over a single policy.md surface
Synthetic benchmark for privacy-preserving and fairness-aware ranking under signal loss
82 ready-to-deploy Microsoft Copilot Chat agents — paste the instruction block into Copilot Studio and you're live. Writing, HR, PM, IT ops, Sales, Finance, Engineering & more. RAI reviewed, source-quality labelled, tested. No coding required.
Curated tools, official sources, OSS, templates, and guides for EU AI Act compliance.
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
Référentiel d'évaluation data science responsable et de confiance
A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.
Python library for implementing Responsible AI mitigations.
AWS Certified AI Practitioner (AIF-C01) exam preparation
Skill to generate the knowledge vault for projects using the Ralph loop
When the stakes are high, intelligence is only half the equation - reliability is the other ⚠️
Open-source toolkit for responsible AI: CLI + SDK to scan code, collect evidence, and generate model cards, risk files, evals, and RAG indexes.
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.
Responsible Prompting is an LLM-agnostic tool that aims at dynamically supporting users in crafting prompts that embed responsible intentions and help avoid harmful, adversarial prompts.
AI Firewall & LLM security toolkit - protect your AI applications from prompt injection, jailbreaks, PII leakage, and adversarial attacks
Lightweight ML bias detection toolkit
Auditing algorithmic bias in criminal justice, hiring, lending, healthcare, and welfare: 6 open-source audits, measurable fairness gaps, and concrete fixes.
Taught by AI genius Andrew NG, this course entails the cutting edge topics such as, How generative AI works including what it can and can't do, Common uses cases such as Reading, Writing, and Chatting, Life Cycle of GenAI projects, Advanced Technology options such as RAG, Fine tunning, and Pre-Training, Implications of GenAI on business & Society.
Full-stack air quality analytics platform built with FastAPI, React, and MySQL. Aggregates multi-source PM2.5/PM10 data, performs multi-city comparison and time-series forecasting (SARIMAX), and integrates an LLM-based planning agent with tiered access, secure APIs, and PDF reporting.
A comprehensive cheat sheet for the AI-900 Azure AI Fundamentals exam covering artificial intelligence workloads, machine learning principles, computer vision, natural language processing (NLP), generative AI, and responsible AI considerations. Includes Azure tools and services with links and logos for visual clarity.
A skills & discipline framework for Microsoft 365 Copilot Cowork — for knowledge workers using AI as a coworker.
🤖🛡️🔍🔒🔑 Tiny package designed to support red teams and penetration testers in exploiting large language model AI solutions.
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.
AI/ML and Generative AI Security Assessment Framework for AWS. Automatically audit Amazon Bedrock and SageMaker & AgentCore workloads for security best practices
Study Guide for the Microsoft Azure AI Fundamentals Exam
Code for the paper "Data Attribution for Text-to-Image Models by Unlearning Synthesized Images."
The course provides guidance on best practices for prompting and building applications with the powerful open commercial license models of Llama 2.