Welcome to qeXAi.com

Evaluate and eXplain systems/models with Quality Engineering

1#AI Assurance Overview

A high-level introduction to AI Assurance and its role in ensuring trust, safety, and reliability in intelligent systems.

5#Bias Detection

Discover tools and frameworks to identify hidden biases in AI models and datasets.

9#Security Audits

Learn how to conduct audits for model security and data privacy vulnerabilities in AI systems.

13#Scenario Analysis

Run simulations to test model behavior across different risk, demographic, or environmental scenarios.

2#Testing Strategies

Explore modern approaches to testing AI systems, from data validation to behavioral robustness testing.

6#Explainable AI

Dive into methods that make model decisions interpretable to humans without compromising performance.

10#Compliance Checks

Review how AI systems align with emerging global regulatory frameworks and industry standards.

14#Model Lifecycle

Manage the full lifecycle of AI models—from design and testing to deployment and retirement.

3#Data Validation Techniques

Learn how to detect and correct data quality issues that can affect model performance and fairness.Ah, that makes sense 😅 — I removed the Masonry component in my last rewrite because you said you wanted a clean 4-column grid, and for classic fixed-column grids, you actually don’t need Masonry. Tailwind’s grid handles responsive columns with uniform gaps perfectly.Here’s the reasoning:

7#Risk Management

A practical guide to identifying, assessing, and mitigating AI-related risks in production environments.

11#Feature Importance

Explore feature attribution techniques to understand which inputs influence AI predictions most.

15#AI Governance

Establish transparent policies, ethical standards, and oversight mechanisms for responsible AI development.

4#Model Robustness

Understand how to evaluate and improve model resilience against noise, drift, and adversarial conditions.

8#Performance Metrics

Understand the right metrics to measure model accuracy, fairness, and real-world effectiveness.

12#Continuous Monitoring

Set up real-time pipelines to detect performance drift, data anomalies, and ethical concerns post-deployment.