ΑΙhub.org
 

Radical AI podcast: featuring Timnit Gebru


by
06 August 2020



share this:
Timnit Gebru

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 Timnit Gebru about “Racial Representation and Systemic Transformation”.

Racial Representation and Systemic Transformation with Timnit Gebru

How do we respond to the racism in the world we have been given? What does it mean to transform technology systems in the spirit of justice and equity? How do we engage with diversity and representation without reducing our efforts to simple branding and lip service? To answer these questions and more the Radical AI Podcast welcomes one of our heroes Dr Timnit Gebru to the show. Timnit Gebru is a research scientist at Google on the ethical AI team and a co-founder of Black in AI. Timnit previously did her postdoc at Microsoft Research for the FATE (Fairness Transparency Accountability and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying any data mining project. She received her PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Full show notes for this episode can be found at Radical AI.

Listen to the episode below:

Relevant links from the episode:

Datasheets for Datasets by Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford.
Black in AI website.

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 :

Royal Statistical Society AI task force says: AI regulation needs statistics

  12 Aug 2026
Real World Data Science interviewed RSS Task Force Chair Donna Philips.

Interview with Akari Asai – beyond scaling: frontiers of retrieval-augmented language models

  11 Aug 2026
Akari Asai describes the power of Augmented Language Models, and how they have culminated in the flagship application for scientific research, Open Scholar.

How generative AI and physics can help design new antibiotics

  10 Aug 2026
We need new antibiotics and designing them is difficult. A potential solution is to use generative AI models, guided by trained scientists.

Congratulations to the #IJCAI2026 award winners

  07 Aug 2026
The winners of three prestigious IJCAI awards for 2026 have been announced.

The Machine Ethics podcast: Safe and moral AI with Rebecca Raper

In this episode Ben chats with Rebecca about AI governance and guardrails, moral assurance, under-specification problems, lack of interdisciplinary work in robotics, and more.

CogTwin: A framework for adaptable digital twins

  05 Aug 2026
Find out more about work presented at IJCAI 2025 on Cognitive Digital Twins.

Forthcoming machine learning and AI seminars: August 2026 edition

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

Healthcare benchmarks are only as good as their assumptions

  03 Aug 2026
In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence