
More than a Glitch, Technochauvanism, and Algorithmic Accountability with Meredith Broussard
The Radical AI PodcastShow Notes
In this episode, we discuss Meredith Broussard's influential new book, More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech – published by MIT Press.
Meredith is a data journalist, an associate professor at the Arthur L. Carter Journalism Institute of New York University, a research director at the NYU Alliance for Public Interest Technology, and the author of several books, including “More Than a Glitch” (which we cover in this episode) and “Artificial Unintelligence: How Computers Misunderstand the World.” Her academic research focuses on artificial intelligence in investigative reporting and ethical AI, with a particular interest in using data analysis for social good.
Full show notes for this episode, including the link to buy Meredith's new book, can be found at Radicalai.org.
More from The Radical AI Podcast
The Radical AI Podcast has unfortunately reached its end. In this episode, Dylan and Jess say goodbye to the podcast and thank you listeners for your unwavering support throughout the years.
In this episode, we interview Dr. Jonathan Flowers about Twitter vs. Mastodon 101, the power dynamics of the fediverse, and potential paths forward in our digital lives.
In this episode, we unpack: is ChatGPT Ethical? In what ways?
ChatGPT – what is it? How does it work? Should we be excited? Or scared? The recent natural language chatbot has been getting ALL the hype. In this episode we interview Deep Dhillon about the ins and outs of ChatGPT! Deep is the co-founder and leader of technology development at Xyonix, where his mission is to find novel value in clients' data. Deep has experience as a technology executive; conceptualizing, architecting and deploying advanced applications, leveraging machine learning, natural language processing and data science to build smarter businesses and more powerful products. Full show notes for this episode can be found at Radicalai.org.
What can our senses teach us about data? What can data teach us about our senses?