Ethical AI: Making Sense of It

Morgan

This show is for managers, marketers, educators, and knowledge workers (30–55) who use ChatGPT-class tools daily and need workplace guardrails, not prompt-engineering tricks or doomsday hype. We own responsible AI policy — disclosure, bias checks, copyright, training-data awareness, and team norms — in copy-paste checklist format. We never compete with `digital-privacy-lets-make-sense-of-this-sht` on password managers and 2FA, or with therapy slugs on AI-anxiety healing narratives. We also avoid the overcrowded "10 ChatGPT hacks" lane. Each episode answers one workplace question with a script or policy paragraph teams can adopt this week. --- Topics include: Ethical AI. Audio for this show is produced with AI assistance. Episodes are researched, scripted, and reviewed for accuracy before release. Become a supporter of this podcast: https://www.spreaker.com/podcast/ethical-ai-making-sense-of-it--7076292/support.

Episodes

  1. Aug 24

    AI in Hiring: Bias Checks Before You Trust the Shortlist

    In this episode, we cover Hiring bias. The conversation opens with: Welcome to Ethical AI: Making Sense of It. I'm Morgan. If you lead a hiring team or review candidate shortlists, you have probably noticed AI tools that promise faster screening. However, those same tools can embed bias before anyone sees the results. Because many platforms train on past hiring records, they sometimes favor certain backgrounds while filtering others. Therefore, a bias check before you trust the shortlist protects fairness in your Listen for the key context, practical takeaways, and the most important points to carry forward. Welcome to Ethical AI: Making Sense of It. I'm Morgan. If you lead a hiring team or review candidate shortlists, you have probably noticed AI tools that promise faster screening. However, those same tools can embed bias before anyone sees the results. Because many platforms train on past hiring records, they sometimes favor certain backgrounds while filtering others. Therefore, a bias check before you trust the shortlist protects fairness in your process. In other words, adding one review step keeps decisions grounded in current standards rather than old patterns. For example, a short note in your team chat can flag when AI contributed to the initial list. Meanwhile, guidance from the EU AI Act and related reports calls attention to these risks in employment tools. Although speed matters, it does not replace human review on protected characteristics. Since your role involves choices that Subscribe for weekly explainers — no guru fluff, just tactics you can apply this week. 📩 Have questions or want to share your experience? Reach out at ethical@senseofthisshit.com. 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: https://www.spreaker.com/podca...

    AI in Hiring: Bias Checks Before You Trust the Shortlist
  2. Jun 6

    Navigate Ethical AI: AI at Work: Disclosure Scripts

    In this episode, welcome to Ethical AI : Let's Make Sense Of This Sh*t. I'm Morgan. If you lead a marketing team or manage projects where tools like copilots now shape first drafts and research summaries, you already know the stakes. Using those systems speeds up routine work, yet it also raises questions about credit, ownership, and client expectations. Welcome to Ethical AI : Let's Make Sense of This Sh*t. I'm Morgan. If you lead a marketing team or manage projects where tools like copilots now shape first drafts and research summaries, you already know the stakes. Using those systems speeds up routine work, yet it also raises questions about credit, ownership, and client expectations. Many organizations still lack clear habits for noting when an assistant contributed text or ideas. Without a simple script, one person might flag the contribution while another stays silent, and the inconsistency creates friction later. Regulators in Europe have started reviewing similar transparency gaps under the EU AI Act summaries, and guidance from the FTC points in the same direction. The result is that teams spend extra time clarifying what came from a person and what came from a model. A short disclosure line placed at the right moment solves mos Subscribe for weekly explainers — no guru fluff, just tactics you can apply this week. 📩 Have questions or want to share your experience? Reach out at ethical@senseofthisshit.com. 💛 Join Our Supporters Club ($3 a month) 💛 Ad-free listening + early episodes — help keep independent media alive. Click Here: https://www.spreaker.com/podca...

    Navigate Ethical AI: AI at Work: Disclosure Scripts

About

This show is for managers, marketers, educators, and knowledge workers (30–55) who use ChatGPT-class tools daily and need workplace guardrails, not prompt-engineering tricks or doomsday hype. We own responsible AI policy — disclosure, bias checks, copyright, training-data awareness, and team norms — in copy-paste checklist format. We never compete with `digital-privacy-lets-make-sense-of-this-sht` on password managers and 2FA, or with therapy slugs on AI-anxiety healing narratives. We also avoid the overcrowded "10 ChatGPT hacks" lane. Each episode answers one workplace question with a script or policy paragraph teams can adopt this week. --- Topics include: Ethical AI. Audio for this show is produced with AI assistance. Episodes are researched, scripted, and reviewed for accuracy before release. Become a supporter of this podcast: https://www.spreaker.com/podcast/ethical-ai-making-sense-of-it--7076292/support.