ΑΙhub.org
 

Radical AI podcast: featuring Lilly Irani


by
08 July 2020



share this:
Lilly Irani

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Lilly Irani about “Labor and Innovation: Exploring the Power of Design and Storytelling”.

Labor and Innovation: Exploring the Power of Design and Storytelling with Lilly Irani

What is the intersection between labor justice movements and the AI technology industry? How can we use design and ethnography to address the relationship between technology, power, and liberation? To answer these questions and more The Radical AI Podcast welcomes Dr Lilly Irani to the show. Dr Lilly Irani is an associate professor of communication and science studies at the University of California, San Diego. She is a cofounder and maintainer of digital labor activism tool Turkopticon, and author of the book Chasing Innovation: Making Entrepreneurial Citizens in Modern India. Dr Irani’s research broadly investigates the cultural politics of high-tech work practices with a focus on how actors produce “innovation” cultures. Full show notes for this episode can be found at Radical AI.

Listen to the episode below:

About Radical AI:

Hosted by Dylan Doyle-Burke, a PhD student at the University of Denver, and Jessie J Smith, a PhD student at the University of Colorado Boulder, Radical AI is a podcast featuring the voices of the future in the field of Artificial Intelligence Ethics.

Radical AI lifts up people, ideas, and stories that represent the cutting edge in AI, philosophy, and machine learning. In a world where platforms far too often feature the status quo and the usual suspects, Radical AI is a breath of fresh air whose mission is “To create an engaging, professional, educational and accessible platform centering marginalized or otherwise radical voices in industry and the academy for dialogue, collaboration, and debate to co-create the field of Artificial Intelligence Ethics.”

Through interviews with rising stars and experts in the field we boldly engage with the topics that are transforming our world like bias, discrimination, identity, accessibility, privacy, and issues of morality.

To find more information regarding the project, including podcast episode transcripts and show notes, please visit Radical AI.




The Radical AI Podcast

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Cybersecurity, evolutionary game theory, and AI safety: an interview with Adeela Bashir

  09 Oct 2026
In the latest of our interviews with the IJCAI-ECAI 2026 doctoral consortium participants, we learn about how cybersecurity attack and defence behaviours evolve.

2026 AAAI / ACM SIGAI Doctoral Consortium interviews compilation

  08 Oct 2026
We collate our interviews with the 2026 cohort of doctoral consortium participants.

Machine learning for clinical time-series forecasting: an interview with Mayra Elwes

  07 Oct 2026
Our series hearing from the IJCAI-ECAI doctoral consortium participants continues.

Can you teach yourself to detect AI writing? Maybe

  06 Oct 2026
Before generative AI, we could generally assume that written text had been composed by a human. This is no longer the case. So how can we spot AI-written text?

Interview with William Yijiang Li: vision language models and the physical world

  05 Oct 2026
How effective are vision language models at understanding how the physical world changes over time?

Forthcoming machine learning and AI seminars: October 2026 edition

  02 Oct 2026
A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.

Rebuilding the brain with neuromorphic computing: an interview with Oliver Rhodes

  01 Oct 2026
Neuromorphic computing takes inspiration from biology to build faster, more energy efficient systems.


↑


AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence