Applied AI Auditing in Python

Focused View

1:05:32

0 View

01 - Introduction

01 - Get responsible with AI Auditing AI systems in Python.mp4

00:48

02 - What you should know.mp4

00:47

03 - Using the exercise files and datasets.mp4

00:51

02 - 1. Introduction to Applied AI Auditing

01 - AI auditing for compliance and fairness.mp4

03:25

02 - AI audit stakeholders.mp4

02:57

03 - Localized fairness and compliance.mp4

03:08

03 - 2. Data Auditing

01 - How to collect benchmark datasets.mp4

02:31

02 - Ethical and inclusive data collection.mp4

02:57

03 - Explore a dataset for representation.mp4

03:09

04 - Data auditing example.mp4

04:19

05 - Challenge Audit a dataset.mp4

01:54

06 - Solution Methods for increasing representation in data.mp4

02:04

04 - 3. AI Model Auditing

01 - Tools for AI audits.mp4

03:01

02 - Scoping an AI audit.mp4

03:28

03 - Model audit setup.mp4

01:58

04 - Audit your classifier for fairness.mp4

03:49

05 - Challenge Audit a classifier.mp4

00:34

06 - Solution Audit a classifier.mp4

01:48

05 - 4. System Audits and Error Analysis

01 - Red teaming.mp4

03:06

02 - Error analysis.mp4

03:41

03 - Challenge Error analysis.mp4

00:37

04 - Solution Error analysis.mp4

01:12

06 - 5. Audit Artifacts

01 - Making audit recommendations.mp4

02:44

02 - Sharing audit results and increasing accountability.mp4

03:16

03 - Algorithmic recourse.mp4

03:01

04 - Algorithmic design history file.mp4

02:40

07 - Conclusion

01 - Thanks for watching.mp4

01:47

Description

AI regulation has arrived, and while there are great high-level strides towards operationalizing AI principles, practitioners must adopt the practice of auditing AI systems to meet compliance and transparency standards. This course guides data scientists and machine learning engineers through the technical process of auditing an AI system, step-by-step. This course is a hands-on, technical course that shows you how to quantify unfairness and disparities to uncover bias and develop fairer AI systems. Instructor Ayodele Odubela explains how to plan, execute, and report on AI audits. Learn the difference between data and algorithm auditing, frameworks for scalable audits, and how context and historical bias play a role in making technical recommendations.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Linkedin Learning

View courses Linkedin Learning

LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.