AI for Beginners: How to Build a Responsible AI Future: AI Governance & Ethics - Ep 7 episode artwork

EPISODE · Jan 24, 2025 · 6 MIN

AI for Beginners: How to Build a Responsible AI Future: AI Governance & Ethics - Ep 7

from AI Innovations Unleashed · host JR DeLaney

The future of AI depends on responsible development. Join us on AI Innovations Unleashed as we discuss the importance of AI governance, ethical considerations, and the role of policymakers, researchers, and the public in shaping an inclusive AI future. Reference ListAngwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica.Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.European Commission. (2021). Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. Partnership on AI. (n.d.). About the Partnership on AI.Additional ResourcesAI4K12: https://ai4k12.org/AI for Good Global Summit: https://aiforgood.itu.int/Center for AI and Digital Policy: https://caidp.org/ Send us Fan MailSupport the showWant to go deeper than the podcast?Join The Unleashed — the growing AI Innovations Unleashed community for educators, parents, students, leaders, and curious minds navigating the future of AI together. Members get deeper discussions, exclusive insights, behind-the-scenes content, early updates, and a place to explore how artificial intelligence is reshaping education and society in real time.AI is moving fast. You don’t have to figure it out alone.Join The Unleashed:https://www.aiinnovationsunleashed.com/become-a-supporter-join-the-unleashed/

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The future of AI depends on responsible development. Join us on AI Innovations Unleashed as we discuss the importance of AI governance, ethical considerations, and the role of policymakers, researchers, and the public in shaping an inclusive AI future. Reference List Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica.Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-...

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AI for Beginners: How to Build a Responsible AI Future: AI Governance & Ethics - Ep 7

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