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AI Regulation and Government Adoption: Insights from Dean Ball of Mercatus Center

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Manage episode 436654700 series 3512115
Kandungan disediakan oleh Alan Pentz and Corner Alliance. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Alan Pentz and Corner Alliance atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode of AI, Government, and the Future, host Alan Pentz engages in a thought-provoking discussion with Dean Ball, Research Fellow at the Mercatus Center, about the complex landscape of AI regulation and its adoption in government.
Dean provides insights into current regulatory efforts, focusing on California's SB 1047 bill and its implications for AI model developers. He explains the challenges of creating liability frameworks for AI and argues for a more nuanced, use-based approach to regulation rather than broad model-based restrictions.
The conversation also delves into the slow adoption of AI in government sectors, with Dean offering perspectives on how to drive meaningful implementation. He emphasizes the importance of measuring success through tangible improvements in efficiency and service delivery, rather than risk mitigation alone.
Dean Ball is a Research Fellow at the Mercatus Center, where he focuses on AI regulation and machine learning. His work provides valuable insights into the intersection of technology, policy, and governance in the rapidly evolving AI landscape.
If you enjoyed this episode, make sure to subscribe, rate and review on Apple Podcasts, Spotify and Google Podcasts, instructions on how to do this are here.
Connect with the host and guest here:

Tune in here:


If you are interested in joining AI, Government, and the Future as a guest, please complete this form: fame.so/cai-guest
AI, Government, and the Future is handcrafted by our friends over at: fame.so
  continue reading

53 episod

Artwork
iconKongsi
 
Manage episode 436654700 series 3512115
Kandungan disediakan oleh Alan Pentz and Corner Alliance. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Alan Pentz and Corner Alliance atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode of AI, Government, and the Future, host Alan Pentz engages in a thought-provoking discussion with Dean Ball, Research Fellow at the Mercatus Center, about the complex landscape of AI regulation and its adoption in government.
Dean provides insights into current regulatory efforts, focusing on California's SB 1047 bill and its implications for AI model developers. He explains the challenges of creating liability frameworks for AI and argues for a more nuanced, use-based approach to regulation rather than broad model-based restrictions.
The conversation also delves into the slow adoption of AI in government sectors, with Dean offering perspectives on how to drive meaningful implementation. He emphasizes the importance of measuring success through tangible improvements in efficiency and service delivery, rather than risk mitigation alone.
Dean Ball is a Research Fellow at the Mercatus Center, where he focuses on AI regulation and machine learning. His work provides valuable insights into the intersection of technology, policy, and governance in the rapidly evolving AI landscape.
If you enjoyed this episode, make sure to subscribe, rate and review on Apple Podcasts, Spotify and Google Podcasts, instructions on how to do this are here.
Connect with the host and guest here:

Tune in here:


If you are interested in joining AI, Government, and the Future as a guest, please complete this form: fame.so/cai-guest
AI, Government, and the Future is handcrafted by our friends over at: fame.so
  continue reading

53 episod

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