What I learned from a Deep Dive into Responsible Data Science and AI
Over two years ago, I wrote a blog post titled 5 Steps to Take as an Antiracist Data Scientist. This post generated a lot of conversation and sparked the beginning of my own knowledge journey with a deep dive into the literature related to ethics, bias, and antiracism in data science and artificial intelligence (AI). I’ve become a big proponent for sharing this knowledge and decided to write a series of blog posts to summarize my findings on this journey.
This series does not need to be read in order and the posts should be considered living documents that may change over time. My knowledge journey continues — feel free to provide feedback, comments, or ideas.
When I first began on my knowledge journey, I wasn’t sure where to start. Explainable AI? AI for social good? This post, the first in a two-part series, reflects my research into understanding who is using these terms and how they are being used.
As a follow up to the first post on AI adjectives, this post seeks to provide definitions for the varying adjectives currently used in this research space. Although definitions are sourced from the literature, many are ambiguous and often overlapping.
3 Major Responsible AI Incidents that Data Scientists Can Learn From
A sample of major ethics and bias incidents that are well known and frequently referenced in the responsible data science and AI space.
I Want to Learn More About Responsible Data Science and AI: A Recommended Reading List
A summary of books and resources that are launching points for learning more about responsible data science with a focus on non-technical resources including books, reports, and films.
12 Conferences Related to Responsible Data Science and AI
Conferences are a great way to learn more about a topic and meet other people interested in similar topics. This list is a starting point for discovering conferences related to responsible data science.
7 Big Open Issues in Responsible Data Science and AI
A non-exhaustive list of some of the biggest open questions and challenges in the responsible data science and AI research and application space.
Disclaimer: Support for this blog series was provided by RTI International. The opinions expressed by the author are their own and do not represent the position or belief of RTI International. Material in this blog post series may be used for educational purposes. All other uses including reprinting, modifying, and publishing must obtain written consent.
