There are 3 repositories under ai-ethics topic.
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'
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
Replication Code for: On the Mechanics of NFT Valuation: AI Ethics and Social Media
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.
Reviews of papers related to robot learning, computer vision, audio , NLP and AI Ethics
Resources on tech and AI ethics.
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 learning model, fairness gives us a way to understand the practical implications of deploying the model in a real-world situation.
Social and Ethical Issues in Information Technology - material and project
Stash of some of the most potent research papers, blogs and videos on AI which I liked.
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.
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.
Explore practical tools to guide the moral design of AI systems.
Practical and Ethical Considerations for Generative AI in Medical Imaging
Debiasing methods on contextualised embeddings are ineffective - CS475
We aim at developing a framework to promote the integration of CDR principles in the legal sector combining the LLI digital ethics code with a practical ethical legal innovation roadmap.
Artificial Intelligence Attitude and Literacy Framework
KLEP (Key-Lock-Executable-Process) is a groundbreaking AI framework that utilizes symbolic AI for dynamic decision-making. It integrates keys, locks, executables, and processes to foster ethical, modular, and transparent AI applications, offering a novel approach for developers and researchers in AI and cognitive science.
Replication Code for: AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security
Knowledge representation model for educational retrieval-augmented generation systems
Reinforcement learning environment for learning ethical behaviours in a SmartGrid use-case.
Comparing AI policies and strategies.
Guidelines and frameworks for ethical AI integration and management in progressive organizations.
My notes and notebooks for Kaggle learning courses.
I spend over ten years writing code and applying math and science. in each keystroke I found joy. I see life is a system that has variable entropy (E). Every process (p[i]) generates dE and my job is to understand what dE(p[i]) means.
A prettified page for MIT's AI Risk Database