ΑΙhub.org
 

Radical AI podcast: featuring Anna Lenhart


by
23 February 2021



share this:
Anna Lenhart

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 Anna Lenhart about congress and the tech lobby.

Anti-trust: congress and the tech lobby with Anna Lenhart

What should you know about anti-trust regulation nationally and internationally? How does the tech sector drive policy? In this episode we interview Anna Lenhart

Anna Lenhart is a researcher for technology policy and democracy at University of Maryland’s iSchool Ethics & Values in Design Lab. She recently served as a TechCongress Fellow with the House Judiciary Committee Antitrust Subcommittee and supported the investigation into Facebook, Google, Amazon and Apple.

Follow Anna Lenhart on Twitter @AnnaCLenhart.

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

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

AI chatbots can effectively sway voters – in either direction

  12 Mar 2026
A short interaction with a chatbot can meaningfully shift a voter’s opinion about a presidential candidate or proposed policy.

Studying the properties of large language models: an interview with Maxime Meyer

  11 Mar 2026
What happens when you increase the prompt length in a LLM? In the latest interview in our AAAI Doctoral Consortium series, we sat down with Maxime, a PhD student in Singapore.

What the Moltbook experiment is teaching us about AI

An experimental social media platform where only AI bots can post reveals surprising lessons about artificial intelligence behaviour and safety.

The malleable mind: context accumulation drives LLM’s belief drift

  09 Mar 2026
LLMs change their "beliefs" over time, depending on the data they are given.

RWDS Big Questions: how do we balance innovation and regulation in the world of AI?

  06 Mar 2026
The panel explores the tensions, trade-offs and practical realities facing policymakers and data scientists alike.

Studying multiplicity: an interview with Prakhar Ganesh

  05 Mar 2026
What is multiplicity, and what implications does it have for fairness, privacy and interpretability in real-world systems?

Top AI ethics and policy issues of 2025 and what to expect in 2026

, and   04 Mar 2026
In the latest issue of AI Matters, a publication of ACM SIGAI, Larry Medsker summarised the year in AI ethics and policy, and looked ahead to 2026.

The greatest risk of AI in higher education isn’t cheating – it’s the erosion of learning itself

  03 Mar 2026
Will AI hollow out the pipeline of students, researchers and faculty that is the basis of today’s universities?



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence