ai-fairness
There are 1 repository under ai-fairness topic.
zhihengli-UR / StyleT2I
Official code of "StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis" (CVPR 2022)
Language:Python 37Official code of "Discover the Unknown Biased Attribute of an Image Classifier" (ICCV 2021)
Language:Python 19- Trusted-AI-Workshops
IBMDeveloperUK / Trusted-AI-Workshops
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
Language:Jupyter Notebook 12 mahmoodlab / CPATH_demographics
Demographic bias in misdiagnosis by computational pathology models - Nature Medicine
Language:Python 12mirianfsilva / ai-fairness
Notes, references and materials on AI Fairness that I found useful and helped me in my academic research.
FairWell-dev / FairWell
FairWell is a Responsible AI tool developed using Streamlit
Language:Jupyter Notebook 3IBMDeveloperMEA / AI-Ethics
Fairness in data, and machine learning algorithms is critical to building safe and responsible AI systems from the ground up by design. Both technical and business AI stakeholders are in constant pursuit of fairness to ensure they meaningfully address problems like AI bias. While accuracy is one metric for evaluating the accuracy of a machine learning model, fairness gives us a way to understand the practical implications of deploying the model in a real-world situation.
jolares / ai-ethics-fairness-and-bias
Sample project using IBM's AI Fairness 360 is an open source toolkit for determining, examining, and mitigating discrimination and bias in machine learning (ML) models throughout the AI application lifecycle.
RishiDarkDevil / Regularization-Based-Fair-Classifier
Here we deal with the issue of fairness in machine learning classification algorithm and we try to exploit regularization technique to attain fairness.
Language:Jupyter Notebook 1HandcartCactus / The-Modeler-Manifesto-Model-Card
A model card inspired by Derman & Wilmott's "Modelers' Hippocratic Oath", adapted for responsible and nuanced ML.
0heyaudace / communities_and_crime
Deep-Learning approach for generating Fair and Accurate Input Representation for crime rate estimation in continuous protected attributes and continuous targets.
Language:Jupyter Notebook 0ankushjain2001 / Fairness-Evaluation-Of-Word-Embeddings
A benchmark of the different word embedding techniques on fairness and bias in AI models
Language:Jupyter NotebookExplainability of AI models is a difficult task which is made simpler by Cortex Certifai. It evaluates AI models for robustness, fairness, and explainability, and allows users to compare different models or model versions for these qualities. Certifai can be applied to any black-box model including machine learning models, predictive models and works with a variety of input datasets.
Language:Jupyter Notebook