Awesome Audit Algorithms is a curated list of algorithms and in-depth research papers on auditing "black-box" AI systems, aimed at enhancing transparency and fairness.
Why It Matters
In today's technological landscape, many critical AI algorithms, ranging from product recommendations to credit scoring, are operated by third-party providers. This often leaves users and organizations without insight into how these algorithms process their data. This repository provides a valuable resource, gathering auditing algorithms specifically designed for "black-box" scenarios to help you better understand internal workings and ensure the fairness and transparency of these AI systems without requiring source code access.
Who It's For
This collection is especially useful for AI researchers, machine learning engineers, AI ethicists, and anyone working with or interested in assessing the fairness and transparency of independent AI models. If you need to audit, monitor performance, or identify potential biases in a third-party AI API without direct access to its source code or training data, this is a must-read reference.
Quick Comparison
* arXiv: A platform hosting a massive repository of scientific pre-prints where you can find thousands of papers related to AI, ethics, and algorithmic auditing. * Google Scholar: A specialized search engine for academic literature, allowing you to easily discover and access AI auditing research from multiple sources. * FAT/ML Conference: Conferences and workshops specialized in Fairness, Accountability, and Transparency in Machine Learning, offering the latest research and in-depth discussions on the subject.
How to Get Started
You can start exploring the research papers, which are clearly categorized by publication year in this repository. Browse the list of papers, learn about various auditing methods, and apply these insights to your own AI algorithm evaluation tasks.
Repo: erwanlemerrer/awesome-audit-algorithms • ★