{"podcast":{"title":"AI Engineering Podcast","slug":"ai-engineering-podcast","podcast_index_feed_id":5875646,"rss_url":"https://serve.podhome.fm/rss/c9abdd38-a5dc-5eb2-96fd-f833f93208a7","website_url":"https://www.aiengineeringpodcast.com","image_url":"https://assets.podhome.fm/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638557211890591941ai_engineering_podcast_logo.jpg","author":"Tobias Macey","episode_count":79,"summary":"This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/ai-engineering-podcast"},"episode":{"title":"Considering The Ethical Responsibilities Of ML And AI Engineers","slug":"considering-the-ethical-responsibilities-of-ml-and-ai-engineers","published_at":"2024-01-28T19:00:00+00:00","page_url":"https://stenobird.com/podcast/ai-engineering-podcast/considering-the-ethical-responsibilities-of-ml-and-ai-engineers","show_page_url":"https://stenobird.com/podcast/ai-engineering-podcast","url":"https://www.aiengineeringpodcast.com/ml-ai-ethical-considerations-episode-27","audio_url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638530538935475186ce719be1-47d3-4af9-a2ef-8b7c0ae2ca6fv2.mp3","summary":"Summary Machine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are necessary to ensure that this next generation of technical and economic systems are equitable and beneficial for the people that they impact. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Nicholas Cifuentes-Goodbody about the different elements of the machine learning workflow where ethics need to be considered Interview Introduction How did you get involved in machine learning? To start with, who is responsible for addressing the ethical concerns around AI? What are the different ways that AI can have positive or negative outcomes from an ethical perspective?  What is the role of practitioners/individual contributors in the identification and evaluation of ethical impacts of their work? What are some utilities that are helpful in identifying and addressing bias in training data? How can practitioners address challenges of equity and accessibility in the delivery of AI products? What are some of the options for reducing the energy consumption for training and serving AI? What are the most interesting, innovative, or unexpected ways that you have seen ML teams incorporate ethics into their work? What are the most interesting, unexpected, or challenging lessons that you have learned while working on ethical implications of ML? Wh…","meta_description":"Summary Machine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound chang…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2367,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/considering-the-ethical-responsibilities-of-ml-and-ai-engineers/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/ai-engineering-podcast/considering-the-ethical-responsibilities-of-ml-and-ai-engineers.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}