Kimberly and Jessica are taking a short break from new episodes, so this week we're re-airing one of our favorites, our conversation with Clara Hawking. Clara is a computer scientist, philosopher, and AI governance expert working in K-12 and beyond.

Why now? Since we recorded this, the law Clara champions in this episode has crossed the finish line. On August 2, 2026, the EU AI Act became fully enforceable, making the EU the first jurisdiction to impose comprehensive, binding regulation on AI systems, even as Brussels negotiates a "Digital Omnibus" that would delay the Act's most demanding high-risk provisions until compliance standards are actually ready. And here in the US, the state-by-state patchwork Kimberly and Clara discuss is now itself contested by a House discussion draft called the Great American AI Act, would temporarily preempt state and local laws regulating AI model development for three years. Everything Clara says about trust, risk, and who gets harmed when governance fails has only gotten more current.

In this episode:

• What AI governance actually is: not IT, not cybersecurity, but behavior and culture
• GDPR and the EU AI Act, explained plainly: rights-based vs. risk-based regulation
• Why parents can't give informed consent about their kids' data, and what an uploaded IEP could cost a child in 20 years
• The convergence phase: AI, biotech, robotics, and quantum computing feeding into each other, and why converged risk compounds instead of adding up
• Trust as the bottleneck for AI adoption, from Jessica's Tesla to recidivism algorithms
• Clara's "me first" governance advice: why are you using this technology, and can you justify the answer?
• Peach and Pit

Links

• EU AI Act resource site — Future of Life Institute's tracker, the most readable overview.
• The EU's official AI Act page — European Commission.
• The Regulatory Tide Goes Out — Jones Walker on the Digital Omnibus and global retrenchment. Good "what's changed since we recorded" companion.
• Great American AI Act discussion draft summary — National Association of Counties on the federal preemption proposal.

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Jessica Parker returns to the show (ha!) for a conversation about Screen People by Atlantic staff writer Megan Garber. The book examines how American life has reorganized itself around screens, and what happens when we can no longer reliably distinguish people from performers or information from entertainment.

We trace Garber’s argument from Marshall McLuhan’s “the medium is the message” through Neil Postman’s “the medium is the metaphor” to her own claim that “the medium is the moral.” And we stake our own claim with THE MEDIUM IS THE MIDDLEMAN.

Along the way, we discuss how scientific findings lose nuance as they travel from research papers to press releases, headlines, and chatbots; why experts hedge while algorithms reward certainty; and how AI magnifies communication patterns already embedded in internet culture.

We also explore the difference between a public and an audience, asking whether personalized AI systems can influence an entire population while preventing the shared discourse necessary for collective action.

In this episode:

• Why screens reward performance over accuracy
• How hedging signals scientific care—not weakness
• What gets lost between a research paper and a chatbot
• AI as a mirror of internet culture
• The commodification of attention
• How audiences differ from active publics
• Why information degradation may be one of AI’s greatest risks
• Small linguistic distortions that are harder to detect than visual deepfakes

In our closing “Pit and Peach,” Jessica reflects on egg retrieval, difficult decisions, and finding clarity, while Kimberly shares how she is rethinking gratitude through the practice of radical gratitude.

Mentioned in this episode:

• Screen People: How We Entertained Ourselves Into a State of Emergency — Megan Garber
• Marshall McLuhan — official site
• Amusing Ourselves to Death — Neil Postman
• On Being with Krista Tippett
• Your Undivided Attention — Tristan Harris and Aza Raskin
• Krista & Tristan's chat, "Can AI be build in service of life?"
• Melody Beattie — official website
• Radical Acceptance — Tara Brach
• Research article by Denise Coberley and Emily Dux Speltz on Scientific Uncertainty in Language Comparing Human and Artificial Texts
• Our Frontiers in Education article on AI as an intermediary

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Part two of two with Roz Hirsch.

Roz has been applying for jobs and not getting interviews she would once have gotten easily. She's been rejected in under an hour. She's been rejected at midnight, by companies where nobody was awake to read anything.

Her expertise is language assessment, so she makes the argument nobody else is making. A resume is an assessment. Assessments require a validity argument, meaning evidence that the decisions you make with them are the right decisions. Validity is a property of the decision, not of the instrument. And validation has to happen at every single company that adopts a screening tool, because a tool validated somewhere else for something else has not been validated for you.

So, has anyone gone back and reread the rejections? Roz cites hiring managers who did, after two full cycles that produced no hires, and found people who should not have been rejected. That's a validity failure, and the near-instant rejection timestamps suggest nobody is checking.

We also get into what the screen is actually reading. Roz's point is that it isn't only the resume and cover letter. It's postal code, financial history, whatever else is available, and the inference that nobody from that postal code works here so this person probably won't either. I bring in Uber's pickers and ants, airline pricing, and the casual nursing algorithms that offer lower wages to people whose credit history says they'll accept.

Then the same argument turned on education. Roz on the professor whose take-home midterm produced near-perfect scores and whose in-class final didn't, and why the bell curve was the problem before AI ever showed up. Why she doesn't have a cheating problem. And the classroom exercise she runs with AI image generation, where students ask for one bear and keep getting several.

Links

• Roz's Random Ramblings: Language, History, and Other Adventures
• Roz on LinkedIn
• Women Writin' 'Bout AI
• Carol Chappelle, Iowa State and The Applied Linguistics Encyclopedia
• Image Description Games: Twin Pics, Say What You See, and Promptle
• Enshittification by Cory Doctorow and our show about the same
• Uber pickers and ants
• The Brown Professor story about AI and cheating
• The TOEFL (Test of English as a Foreign Language)
• Part one of Kim & Roz talkin' 'bout AI

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