AI and tech ethics resources

I follow as much discourse around ethics in machine learning, data analysis, and artificial intelligence as I can. These are the resources I’ve used over the years to help me gather knowledge and perspectives and form my own opinions about these types of technology and implementations.

I’ve co-presented two talks on machine learning bias, and gave another on my own about the effects of missing data on data analysis. The slides for those talks can be found on About Sarah.

As an individual person, I have specific interests and biases that inform what content goes from “encountered in my news feed or RSS reader” to “opened in a new tab” to “actually read the thing”. This list is categorized according to those loose interests and resultant taxonomy. Some items might fall into multiple categories.

I’ve denoted things as follows:

I’ve also included some content that I haven’t yet made the time to consume, denoted with a ⌛️ emoji.

Dataset creation and curation, including data labeling #

Representation in tech and machine learning, globally, linguistically, racially, or otherwise #

Machine learning bias, especially for decision-making #

Machine learning development and implementation #

Auditing, testing, and monitoring machine learning #

Alternate approaches to data-driven and ML-driven systems #

General resources #