A web app for ethical AI framework, internal evaluation workflow and monitoring production model
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Updated
Jun 27, 2022 - CSS
A web app for ethical AI framework, internal evaluation workflow and monitoring production model
Model check systems for obligations
Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
Micro AI for multiple LLM switching, preparing datasets, training models, and deploying them in isolated environments using Docker
This project aims to understand the algorithmic bias in the corrections system through analyzing the COMPAS dataset.
The first ethical language generation model.
A benchmark of the different word embedding techniques on fairness and bias in AI models
A bias bounty competition for income prediction. Using the pointer decision list method to improve group accuracy.
People for Ethical Treatment of the Artificial Intelligence
The simulation of fuzzy ethical rules for drones.
DAD/Do's and Dont's for Machine Learning Engineers
An Ethical Engine that decides between saving the lives of passengers in a car or pedestrians given precarious scenarios.
How we will use sound, geothermal energy, and educational games to power the ledger. And make wifi ridiculously fast
Collection of libraries related to Ethical AI, Fairness & Transparency
Welcome to the "Harnessing the Power of AI" workshop! This GitHub repository serves as a comprehensive resource for participants seeking hands-on learning and in-depth understanding of AI concepts, techniques, and tools.
Giving voice to the voiceless and helping voiceless to be heard
Social and Ethical Issues in Information Technology - material and project
Code for fair-representation learning path
Basic tutorials and code for teaching deep learning and machine learning
Ethical questions, risks and issues to think about to help create responsible AI products and services
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