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

Example projects for Arthur Model Monitoring Platform

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.

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 le…

Introduction to simple machine learning, deep learning and geometric deep learning concepts and methods.

Comparative Analysis of Bi-Directional Long Short-Term Memory and BERT Models for Fake News Detection with Explainable AI Using Lime

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.

Comprehensive LLM testing suite for safety, performance, bias, and compliance, equipped with methodologies and tools to enhance the reliability and ethical integrity of models like OpenAI's GPT series for real-world applications.

University of Piraeus - Thesis Project

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 …