{"owner":"Trusted-AI","github":"https://github.com/Trusted-AI","claimed":false,"inventory":[],"indexed":[{"repo":"Trusted-AI/adversarial-robustness-toolbox","github":"https://github.com/Trusted-AI/adversarial-robustness-toolbox","description":"Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams","language":"Python","stars":6183,"topics":["python","attack","adversarial-machine-learning","poisoning","trusted-ai","artificial-intelligence","extraction","adversarial-attacks","adversarial-examples","evasion"],"license":"MIT","category":"machine-learning"},{"repo":"Trusted-AI/AIF360","github":"https://github.com/Trusted-AI/AIF360","description":"A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.","language":"Python","stars":2852,"topics":["ai","fairness-ai","fairness","fairness-testing","fairness-awareness-model","bias-detection","bias","bias-correction","bias-reduction","bias-finder"],"license":"Apache-2.0","category":"machine-learning"}],"how_to_buy":"GET /r/Trusted-AI/<repo> (Accept: application/json) for any listed repo here: tree, README, price and the checkout to pay (x402; rehearse first at its test twin, simulated money). Repos under 'indexed' are free: clone them from GitHub."}