GitHub topics: ai-explainability
arthur-ai/arthur-sandbox 📦
Example projects for Arthur Model Monitoring Platform
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ranfysvalle02/crewai-flask-autoresearch
In-depth exploration of Large Language Models (LLMs), their potential biases, limitations, and the challenges in controlling their outputs. It also includes a Flask application that uses an LLM to perform research on a company and generate a report on its potential for partnership opportunities.
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despinakz/xai-tools
University of Piraeus - Thesis Project
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EnriqManComp/Mango-Leaf-Disease-Classification
This project aims to differentiate among various diseases (multiclass prediction) present in mango leaves. Various machine learning techniques were employed in this project to achieve optimal performance in a model capable of predicting multiple classes.
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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
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divanoLetto/Explicability-of-decisions-and-uncertainty-in-Deep-Learning
Implementation of global methods (explain the behavior of a model as a whole) and local methods (respect to a specific decision) that allow to explain why an AI model makes its decisions.
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basiralab/basic-ML-DL-concepts
Introduction to simple machine learning, deep learning and geometric deep learning concepts and methods.
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IBMDeveloperMEA/AI-Ethics Fork of asnajaved/Identify-and-remove-bias-from-AI-models-using-Watson-Studio
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.
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IBMDeveloperMEA/AI-Integrity-Improving-AI-models-with-Cortex-Certifai
Explainability 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.
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