AI Ethics: Disability-Centered Frameworks

Course details

Building disability- and neurodiversity-centered AI systems helps not only the over one billion people with impairments globally, but everyone else, as well. In this course, technology explorer and public voice for the algorithmic spectrum Yonah Welker guides you through disability and AI bias, as well as systems and risks. Learn about disability-centered data, models, knowledge frameworks, and policies. Plus, explore positive ways forward.

Concepts

0. Introduction

01 - Disability-centered systems and algorithms
02 - What you should know

1. Disability and AI Bias

03 - How AI algorithms support people with disabilities
04 - Why disabilities can be challenging for algorithms
05 - How systems can be inaccurate
06 - What factors lead to errors
07 - Assess your problem and objectives

2. Understanding Systems and Risks

08 - AI system categories
09 - AI regulation and protections
10 - How risk categories are unique for people with disabilities
11 - Assess your systems, categories, and risks

3. Disability-Centered Data

12 - How data is used in AI systems
13 - How bias can enter data
14 - Assess your data

4. Disability-Centered Models

15 - How AI models impact people with disabilities
16 - How bias can affect AI models
17 - Assess your AI models

5. Disability-Centered Knowledge Frameworks and Policies

18 - Adopting ethical frameworks, guidelines, and memorandums
19 - Disability-centered framework parameters
20 - Additional tools and frameworks
21 - Assess resources and memorandums

Conclusion

22 - A way forward

AI Ethics: Disability-Centered Frameworks

Authors

Yonah Welker