Paper Summary for Relations between Trustworthy AI Concepts
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Updated
Sep 2, 2022
Paper Summary for Relations between Trustworthy AI Concepts
This repo contains the codes, figures and datasets for the paper - U-Trustworthy Models. Reliability, Competence, and Confidence in Decision-Making.
FairPy: A Python Library for Machine Learning Fairness
Human vs AI: Frontend📱
AI-HCI research project with the aim to study the key factors affecting trust in an AI system recommendations.
DSPLab@UMich-Dearborn Website
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
A robustness study of selected SDG classifiers
Code for the Paper "A Functional Data Perspective and Baseline on Multi-Layer Out-of-Distribution Detection"
We make Generative AI accessible to Federal agencies and businesses. Easy-to-use ezGPT™ platform eliminates the need for in-house expertise and delivers pre-built solutions for rapid innovation. With security and privacy at its core, we unlock the potential of AI. Our innovative chatbot guides users, ensuring a smooth and successful experience.
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).
Unofficial implementation of paper "Flexibly Fair Representation Learning by Disentanglement"
Neural Additive Models - Visualization Tool in PyTorch/Plotly-Dash
Scripts to process the reference framework into an object
This is a project for testing AI bias detection and mitigation methods.
We make Generative AI accessible to Federal agencies and businesses. Easy-to-use ezGPT™ platform eliminates the need for in-house expertise and delivers pre-built solutions for rapid innovation. With security and privacy at its core, we unlock the potential of AI. Our innovative chatbot guides users, ensuring a smooth and successful experience.
Reliable and Trustworthy Intelligence AI notebooks from ETH Zurich course taught by Prof. Dr. Martin Vechev
Fair and explainable ML workshop
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