ΑΙhub.org
 

Radical AI podcast: featuring Emily Bender


by
19 August 2020



share this:

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 Emily Bender about “The Power of Linguistics: Unpacking Natural Language Processing Ethics”.

The Power of Linguistics: Unpacking Natural Language Processing Ethics with Emily Bender

What are the societal impacts and ethics of Natural Language Processing (or NLP)? How can language be a form of power? How can we effectively teach ethics in the NLP classroom? How can we promote healthy interdisciplinary collaboration in the development of NLP products? To answer these questions and more we welcome Dr Emily Bender to the show. Emily researches linguistics, computational linguistics, and ethical issues in Natural Language Processing. Emily is currently a Professor in the Department of Linguistics and an Adjunct Professor in the Department of Computer Science and Engineering at the University of Washington. She is also the faculty director of the CLMS program and the director of the Computational Linguistics Laboratory. 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 :

When AI art has no author: Study finds generated images often can’t be traced to training data

  14 Sep 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

AI in nature conservation: powerful tool or dangerous shortcut?

  11 Sep 2026
AI provides opportunity for future biodiversity conservation but introduces risks .

Improving the process for large-scale recommender systems: an interview with Haruka Kiyohara

  10 Sep 2026
Credit-assigned policy gradient for early stage retrieval in two-stage ranking.

The Machine Ethics podcast: Data Collective with E.M. Lewis-Jong

Ben chats to E.M. Lewis-Jong about the promise of AI and making human connection easier, speech recognition and supporting linguistic diversity, making useful technologies that have a purpose, and more.

AI for ethology: an interview with Isla Duporge

  08 Sep 2026
Deep learning is becoming a powerful tool for understanding animal behaviour and tracking populations.

AI in cardiology: The path to practical application carries risks

  07 Sep 2026
Can artificial intelligence help us better combat cardiovascular diseases? Legal researcher Hannah van Kolfschooten urges caution, as there are still many legal issues that need to be resolved.

AI-powered camera system offers low-cost way to monitor bumblebees

  04 Sep 2026
Researchers have developed a semi-automated method that uses remote cameras to survey bumblebees and potentially other insects.

Interview with Noah Golowich – theoretical foundations for learning in games and dynamic environments

  03 Sep 2026
Noah Golowich tells us about his research into the theory of decision making and learning in games, which have applications in Multi-Agent Reinforcement Learning.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence