{"repo":"microsoft/responsible-ai-toolbox","free":true,"listed":false,"github":"https://github.com/microsoft/responsible-ai-toolbox","clone":"git clone https://github.com/microsoft/responsible-ai-toolbox.git","description":"Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.","language":"TypeScript","stars":1820,"topics":["ui","responsible-ai","data-science","fairness","fairness-ml","fairness-ai","explainable-ai","explainable-ml","explainability","machinelearning"],"license":"MIT","category":"ui-components","readme_excerpt":"Responsible AI Toolbox Responsible AI is an approach to assessing, developing, and deploying AI systems in a safe, trustworthy, and ethical manner, and take responsible decisions and actions. Responsible AI Toolbox is a suite of tools providing a collection of model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions. The Toolbox consists of three repositories: Repository Tools Covered -- -- Responsible-AI-Toolbox Repository (Here) This repository contains four visualization widgets for model assessment and decision making: 1. Responsible AI dashboard, a single pane of glass bringing together several mature Responsible AI tools from the toolbox for a holistic responsible assessment and debugging of models and making informed business decisions. With this dashboard, you can identify model errors, diagnose why those errors are happening, and mitigate them. Moreover, the causal decision-making capabilities provide actionable insights to your stakeholders and customers. 2. Error Analysis dashboard, for identifying model errors and discovering cohorts of data for which the model underperforms. 3. Interpretability dashboard, for understanding model predictions. This dashboard is powered by InterpretML. 4. Fairness dashboard, for understanding model’s fairness i","default_branch":null,"files":null,"tree":[],"storefront":"/r/microsoft","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/microsoft/responsible-ai-toolbox/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}