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fairness
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15 search results found
Awesome Machine Learning Interpretability
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3,241
A curated list of awesome responsible machine learning resources.
Dalex
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1,242
moDel Agnostic Language for Exploration and eXplanation
Responsible Ai Toolbox
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1,187
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.
Interpretable_machine_learning_with_python
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629
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Mli Resources
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405
H2O.ai Machine Learning Interpretability Resources
Fairlens
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76
Identify bias and measure fairness of your data
Deep Explanation Penalization
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74
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Machine Learning Ethics References
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54
List of references about Machine Learning bias and ethics
Responsibly
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41
Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
Ethicml
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25
Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency
Equalityml
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24
Evidence-based tools and community collaboration to end algorithmic bias, one data scientist at a time.
Interpretable Ml
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17
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Automlx
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16
This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.
Hc_ml
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12
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Trusted Ai Workshops
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9
Introduction to trusted AI. Learn to use fairness algorithms to reduce and mitigate bias in data and models with aif360 and explain models with aix360
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