エピソード

  • AI in the Classroom: Policy Language That Works in 2026

    AI in the Classroom: Policy Language That Works in 2026
    2026/07/14
    In this episode, in a world where AI is making life-altering decisions faster than we can spell "accountability," figuring out what's actually ethical feels like decoding a glitchy algorithm. This episode cuts through the hype and hand-wringing to tackle the messy reality of building AI that doesn't screw people over.

    We unpack the real trade-offs between speed and safety, why bias keeps sneaking into "neutral" systems, and how companies (and regulators) are fumbling—or finally stepping up—their responsibility. You'll walk away with a clearer lens on the frameworks that actually work versus the ones that just sound good in a press release.

    Ready to stop guessing and start understanding? Hit play, then subscribe so you never miss an episode that makes the AI chaos a little less chaotic.

    📩 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/podcast/ethical-ai-let-s-make-sense-of-this-sh-t--7076292/support
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    8 分
  • Shadow AI Tools: How to Audit What Your Team Already Uses

    Shadow AI Tools: How to Audit What Your Team Already Uses
    2026/07/14
    In this episode, in a world where AI is making life-or-death decisions faster than regulators can blink, Ethical AI: Let's Make Sense Of This Sht cuts straight through the hype and hand-wringing to ask what "doing the right thing" actually looks like when the code ships.

    This episode digs into the real friction points—algorithmic bias that quietly screws over entire demographics, the privacy tightrope companies walk between useful data and creepy surveillance, and the accountability gaps that leave no one responsible when models go off the rails. You'll hear unfiltered takes on whether current ethics frameworks are toothless theater, how organizations can bake genuine safeguards into their pipelines without killing innovation, and why ignoring these issues now guarantees bigger headaches (and lawsuits) later.

    Ready to stop nodding along at buzzwords and start making sense of the mess? Hit play, then drop your own hot takes in the comments—we're listening.

    📩 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/podcast/ethical-ai-let-s-make-sense-of-this-sh-t--7076292/support
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    8 分
  • Understanding AI Hallucination Risk: Review Steps Before Client

    Understanding AI Hallucination Risk: Review Steps Before Client
    2026/06/30
    In this episode, we cover Hallucinations. The conversation opens with: Welcome to Ethical AI : Let's Make Sense Of This Sh*t. I'm Morgan. Team leads and marketers who send AI generated drafts straight to clients face a clear problem. AI hallucination risk turns a fast draft into a liability when facts shift without warning. Listen for the key context, practical takeaways, and the most important points to carry forward.

    Welcome to Ethical AI : Let's Make Sense Of This Shit. I'm Morgan. Team leads and marketers who send AI generated drafts straight to clients face a clear problem. AI hallucination risk turns a fast draft into a liability when facts shift without warning. However, these errors often hide inside confident sentences that read well at first glance. For example, a report might invent a regulation date or misstate a competitor statistic, and the mistake travels straight to the client inbox. Meanwhile, guidance from the EU AI Act and NIST frameworks stresses human checks on outputs used in professional settings. The reality is that speed without review creates downstream fixes that cost time and trust. In other words, a short verification pass before delivery catches the issues while they remain internal.

    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...
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    9 分
  • Understanding AI Vendor Due Diligence: Five Questions Before You

    Understanding AI Vendor Due Diligence: Five Questions Before You
    2026/06/29
    In this episode, we cover Vendor review. The conversation opens with: Welcome to Ethical AI : Let's Make Sense Of This Sh*t. I'm Morgan. Listen for the key context, practical takeaways, and the most important points to carry forward.

    Welcome to Ethical AI : Let's Make Sense Of This Shit. I'm Morgan. Many teams now face a common problem when they add new software because AI features hide inside most modern SaaS tools. Team leads and marketers often sign contracts based on price or speed alone however they later discover hidden risks around data use and bias. This episode focuses on AI vendor due diligence and gives you five practical questions to ask before any purchase. Educators and knowledge workers share the same challenge because a quick decision can expose the whole group to copyright issues or unexpected training data practices. The reality is that regulators already watch these deals closely therefore skipping review now costs more later.

    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...
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    8 分
  • Understanding Copyright and AI Output: What Small Teams Can

    Understanding Copyright and AI Output: What Small Teams Can
    2026/06/06
    In this episode, ever feel like AI is rewriting the rules of society faster than we can agree on what “fair” even means? In this no-BS episode of Ethical AI: Let’s Make Sense Of This Sh*t, we cut through the hype and the hand-wringing to confront the real ethical landmines hiding in the code we’re already using every day.

    We dig into algorithmic bias that quietly shapes hiring, lending, and policing, unpack who’s actually accountable when an AI screws up, and explore whether regulation can keep pace without killing innovation. You’ll hear straight talk on transparency, data consent, and the uncomfortable truth that “ethical AI” isn’t a feature you toggle—it’s a constant, messy negotiation we all have to join.

    Ready to stop nodding along at AI headlines and start thinking critically about what’s next? Hit play, then drop your toughest question in the comments—we’ll tackle the best ones in future episodes.

    📩 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...
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    6 分
  • Understanding AI at Work: Disclosure Scripts When You Used

    Understanding AI at Work: Disclosure Scripts When You Used
    2026/06/06
    In this episode, we cover Disclosure. The conversation opens with: 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. Listen for the key context, practical takeaways, and the most important points to carry forward.

    Welcome to Ethical AI : Let's Make Sense Of This Shit. 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...
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    6 分
  • Understanding Ethical AI 101: What "Responsible Use" Means

    Understanding Ethical AI 101: What "Responsible Use" Means
    2026/06/06
    In this episode, ever wondered why so many AI systems quietly reinforce bias or cross ethical lines without anyone noticing—until it's too late? In this episode of Ethical AI: Let's Make Sense Of This Sh*t, we cut through the hype and jargon to expose the real-world traps that trip up developers, businesses, and policymakers alike. You'll get blunt insights on spotting hidden bias in training data, building accountability into every model layer, and turning vague "do good" principles into concrete, testable practices that actually stick.

    From case studies of AI gone wrong to actionable frameworks you can apply tomorrow, we break down how to navigate privacy landmines, transparency demands, and the growing regulatory mess without losing your mind—or your budget. Expect straight talk on trade-offs, red flags to watch for in vendor tools, and why "ethics washing" fails every time.

    Ready to stop guessing and start building AI that holds up under scrutiny? Hit play, then drop your toughest ethical dilemma in the comments so we can tackle it next.

    📩 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...
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    8 分