The Radical AI Podcast
A podcast by Radical AI
Categories:
91 Episodes
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Stay Radical: A Final Goodbye from Dylan and Jess
Published: 8/9/2023 -
Twitter vs. Mastodon with Johnathan Flowers
Published: 4/26/2023 -
More than a Glitch, Technochauvanism, and Algorithmic Accountability with Meredith Broussard
Published: 3/22/2023 -
The Limitations of ChatGPT with Emily M. Bender and Casey Fiesler
Published: 3/1/2023 -
ChatGPT: What is it? How does it work? Should we be excited? Or scared? with Deep Dhillon
Published: 1/25/2023 -
Sounds, Sights, Smells, and Senses: Let’s Talk Data with Jordan Wirfs-Brock
Published: 11/30/2022 -
How to Stay Safe Online with Seyi Akiwowo
Published: 10/26/2022 -
Data Privacy and Women’s Rights with Rebecca Finlay
Published: 9/28/2022 -
Digital Lethargy with Tung-Hui Hu
Published: 8/31/2022 -
Should the Government use AI? with Shion Guha
Published: 7/27/2022 -
Envisioning a Decolonial Digital Mental Health with Sachin Pendse, Munmun De Choudhury, and Neha Kumar
Published: 6/29/2022 -
Visualizing Our Lives Through Data with Jaime Snyder
Published: 5/25/2022 -
Let’s Talk About Sex: Digital Pornography and LGBTQIA+ Censorship w/ Alex Monea
Published: 4/27/2022 -
New Year, New You: Welcome Back to the Radical AI Podcast
Published: 4/20/2022 -
Measurementality #7: Why AI Registries are Critical for Metrics of Accountability with Sara Jordan and Anand Rao
Published: 12/19/2021 -
Decolonial AI 101 with Raziye Buse Çetin
Published: 12/8/2021 -
Design Justice 101 with Sasha Costanza-Chock
Published: 11/3/2021 -
What Causes AI to Fail? with the AI Today Podcast
Published: 10/15/2021 -
Measurementality #6: Authentic Accountability for Successful AI with Yoav Schlesinger
Published: 10/11/2021 -
Predicting Mental Illness Through AI with Stevie Chancellor
Published: 10/6/2021
Radical AI is a podcast centering marginalized or otherwise radical voices in industry and the academy for dialogue, collaboration, and debate regarding the field of Artificial Intelligence Ethics and the relationship between the humanities and machine learning.