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

List of references about Machine Learning bias and ethics

Courses on Kaggle

An Introduction to Transparent Machine Learning

An Introduction to Transparent Machine Learning

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

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

The findings of this research reveal several intriguing disparities between human and AI text generation. I demonstrated that these differences could be successfully utilized by classifiers to distinguish between human and AI-generated text.

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

Reviews of papers related to robot learning, computer vision, audio , NLP and AI Ethics

Replication Code for: On the Mechanics of NFT Valuation: AI Ethics and Social Media

Stash of some of the most potent research papers, blogs and videos on AI which I liked.

Fairness in data, and machine learning algorithms is critical to building safe and responsible AI systems from the ground up by design. Both technical and business AI stakeholders are in constant pursuit of fairness to ensure they meaningfully address problems like AI bias. While accuracy is one metric for evaluating the accuracy of a machine le…

Resources on tech and AI ethics.

This repository provides comprehensive guidelines, frameworks, and sample policies for the ethical and effective integration of AI in progressive organizations. It serves as a platform for discussion and collaboration on AI governance and ethics.

Social and Ethical Issues in Information Technology - material and project

An early version of a system that credits creators based on the similarity of their content to an LLM response. Giving back to creators is the only way for fair, sustainable AI economies that lead to true growth.

Debiasing methods on contextualised embeddings are ineffective - CS475