This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.

Free and open source code of the https://tournesol.app platform. Meet the community on Discord https://discord.gg/WvcSG55Bf3

a comprehensive statistical framework for detecting circular reasoning bias in AI algorithm evaluation

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

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 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.

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.

Worldwide AI Ethics (WAIE) is a systematic literature review done by AIRES researchers at PUCRS.

An Introduction to Transparent Machine Learning

Paper lists about 'Constitutional AI System' or 'AI under Ethical Guidelines'

An Introduction to Transparent Machine Learning

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.

Trustworthy AI: From Theory to Practice book. Explore the intersection of ethics and technology with 'Trustworthy AI: From Theory to Practice.' This comprehensive guide delves into creating AI models that prioritize privacy, security, and robustness. Featuring practical examples in Python, it covers uncertainty quantification, adversarial ML

An analytical essay on why prediction-based models fail in reflexive, unstable systems. This article argues that accuracy collapses when models influence behavior, and proposes equilibrium and force-based modeling as a more robust framework for understanding pressure, instability, and transitions in AI-shaped systems.

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.

An initiative to build a fair and sustainable AI ecosystem by identifying and crediting open-access creators whose work shows strong similarity to AI-generated content.

Code and evaluation framework for assessing discrimination risks of LLMs in HRI tasks (Paper: LLM-Driven Robots Risk Enacting Discrimination, Violence,and Unlawful Actions)