The Radical AI Podcast
Un pódcast de Radical AI
Categorías:
91 Episodo
-
Measurementality #5: Intergenerational Collaboration with Sinead Bovell
Publicado: 19/9/2021 -
Indigenous AI 101 with Jason Edward Lewis
Publicado: 8/9/2021 -
Casteist Technology and Digital Brahminism with Thenmozhi Soundararajan and Seema Hari
Publicado: 18/6/2021 -
Measurementality #4: What are we Optimizing for? with Laura Musikanski and Jonathan Stray
Publicado: 16/6/2021 -
Feminist AI 101 with Eleanor Drage and Kerry Mackereth
Publicado: 2/6/2021 -
Decentralizing AI with Divya Siddarth
Publicado: 27/5/2021 -
Killer Robots and Value Sensitive Design with Steven Umbrello
Publicado: 5/5/2021 -
Measurementality #3: Counting Mental Health and Caregiving in Technology and AI
Publicado: 2/5/2021 -
Design, Disability, Creativity, and Accessibility with Cynthia Bennett
Publicado: 21/4/2021 -
Atlas of AI with Kate Crawford
Publicado: 7/4/2021 -
Defining Bias with Su Lin Blodgett
Publicado: 31/3/2021 -
Measurementality #2: Children's Data and Sustainability
Publicado: 21/3/2021 -
Your Computer Is on Fire with Mar Hicks & Kavita Philip
Publicado: 10/3/2021 -
All Tech is Human Series #9 - Misinformation & Free Expression with Jasmine McNealy & Claire Wardle
Publicado: 3/3/2021 -
Social Inequality in the Digital Economy with Zanele Munyikwa
Publicado: 24/2/2021 -
Measurementality #1: Defining What Counts in the Algorithmic Age
Publicado: 14/2/2021 -
Anti-Trust: Congress and the Tech Lobby with Anna Lenhart
Publicado: 10/2/2021 -
All Tech is Human Series #8 - Improving Social Media: Content Moderation & Democracy with Sarah T. Roberts & Murtaza Shaikh
Publicado: 27/1/2021 -
Ability and Accessibility in AI with Meredith Ringel Morris
Publicado: 20/1/2021 -
2020 Hindsight: The Radical AI Podcast New Years Spectacular!
Publicado: 30/12/2020
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.