This course aims to help learners identify bias in coding and machine learning, teaching them how to code inclusively and providing tips to ensure applications cater to all users. The course covers topics such as understanding bias, avoiding assumptions, inclusive design, and the implications of biased algorithms. The teaching method involves discussing real-world examples and practical tips. This course is intended for developers, data scientists, and anyone involved in creating applications or working with machine learning algorithms.
Overview
Syllabus
Introduction
What is bias
Computers cant be biased
Machine bias
Not making assumptions
Glow Eve
Pool Float
Bad Data
Image Searches
Data Driven Bias
Other Real World Implications
Soap is a Problem
Biased Algorithms
Who is the Average User
Questioning Assumptions
Inclusive Design
oxo Kitchen Products
Web Forms
Questions
Focus Groups
Evaluations
Taught by
PASS Data Community Summit
