Women talkin' 'bout AI

Kimberly Becker & Jessica Parker

Two women examining AI through a lens of power, not just capability. Why deepfakes target women. How bias gets baked in. What tech companies aren't saying. Kimberly brings corpus linguistics; Jessica brings strategy. Both bring skepticism, feminism, research expertise, and a refusal to take the hype at face value.Subscribe to our channel if you’re also interested in understanding AI behind the headlines. 

  1. 6d ago

    How Are AI and Chatbots Changing Truth, Trust, and Public Discourse?

    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 accuracyHow hedging signals scientific care—not weaknessWhat gets lost between a research paper and a chatbotAI as a mirror of internet cultureThe commodification of attentionHow audiences differ from active publicsWhy information degradation may be one of AI’s greatest risksSmall linguistic distortions that are harder to detect than visual deepfakesIn 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 GarberMarshall McLuhan — official siteAmusing Ourselves to Death — Neil Postman On Being with Krista TippettYour Undivided Attention — Tristan Harris and Aza RaskinKrista & Tristan's chat, "Can AI be build in service of life?"Melody Beattie — official websiteRadical Acceptance — Tara BrachResearch article by Denise Coberley and Emily Dux Speltz on Scientific Uncertainty in Language Comparing Human and Artificial TextsOur Frontiers in Education article on AI as an intermediaryLeave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  2. Aug 5

    What Language Assessment Can Teach Us About AI Resume Screening (Part 2 with Roz Hirch)

    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 AdventuresRoz on LinkedInWomen Writin' 'Bout AICarol Chappelle, Iowa State and The Applied Linguistics Encyclopedia Image Description Games: Twin Pics, Say What You See, and PromptleEnshittification by Cory Doctorow and our show about the sameUber pickers and antsThe Brown Professor story about AI and cheatingThe TOEFL (Test of English as a Foreign Language) Part one of Kim & Roz talkin' 'bout AI Leave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  3. Aug 5

    What Happens When You Ask AI and Humans the Same Question (Part 1 with Roz Hirch)

    Kimberly's friend Roz Hirch is the guest on this two-part series. Roz is a linguist, a college instructor in Medicine Hat, Alberta, and a language assessment specialist. She is also out of work for the summer for the first time in her life. Kimberly suggested that she read The Artist's Way by Julia Cameron, and that led to Roz asking ChatGPT and Claude for book recommendations as well. She wrote a question describing herself and her situation and asked ChatGPT and Claude for reading recommendations. Something in ChatGPT's answer bothered her enough that she took the identical question, word for word, and texted it to friends and family to see what people would do with it. The machines gave her thirteen books and seven. The humans gave her one, or two, or none. Three titles appeared on both AI lists. Not one appeared on both an AI list and a human list. The AI books all pointed the same direction, which was creating a portfolio career, company of one, multipotentialite, like build an umbrella and put everything under it. The people who actually know Roz told her to write. Roz and I analyzed the responses from the humans and the bots, and because Roz has a background in theater as well as linguistics, she reaches for the difference between naturalism, which is how people talk, and realism, which is how we think people talk. We look at what humans do that machines don't, such as dropping the subject, hedging in nearly every response, and knowing when to stop, which Grice's maxim of quantity covers and which one model violated thirteen times over. We also analyze the speech act itself, recommendations. A recommendation ordinarily requires the speaker to have read the thing and to stake something on it. The form survives in the AI answers. The function is hollowed out, because there is nobody there to have been inspired. Links  Roz's Random Ramblings: Language, History, and Other Adventures Roz on LinkedIn Women Writin' 'Bout AIJohn Searle, "The Chinese Room"Mini Philosophy, Jonny Thomson, the episode on online versus face-to-face conversationHow to Be Everything, Emilie WapnickRange, David EpsteinThe Wealthy Barber, David ChiltonThe Artist's Way, Julia CameronLeave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  4. Jul 29

    Women Talkin' 'Bout Friction

    Devon Cantwell-Chavez studies global urban climate change governance. She and Kimberly met because of an antagonistic LinkedIn post (not between the two of them), and then discovered that, in many ways, they came up the same way. They both were Teach for America corps members, both early believers in classroom technology, and both landed somewhere far more critical. This conversation is about what gets lost when we design friction out of learning and research.  They get into the myth of the digital native and why "nobody knows how file folders work anymore," the frictionless interfaces that train us to just ask instead of think, and Goodhart's Law, the idea that a measure stops being effective once it becomes a target. Devon lays out how her research team built friction back in on purpose with a no-first-use policy, low-stakes-only translation tools, and a tagging system so every use of AI is on the record.  The episode closes on why AI can't be replicated the way rules-based software can, what that means for qualitative research, and where Devon finds hope, on the lawns of rural Michigan, in t-shirts and yard signs against data centers. Mentioned in this episode: The AI Con, Emily Bender and Alex Hanna Being Wrong: Adventures in the Margin of Error, Kathryn Schulz Right Kind of Wrong: The Science of Failing Well, Amy EdmondsonKimberly's Substack that discusses the following: Don Norman on design responsibility Rosina Lippi-Green on communication as a two-way streetGoodhart's LawDevon's viral LinkedIn post The chess-cheating study (in understandable language) or the research manuscript preprintThe Brown University exam experiment Box Elder County data center coverage Digital NativesNon-Consensual Sexual ImageryDevon's t-shirtCancellation of data center projects: https://www.datacenterwatch.org/reportFact Checking Notes: Traditional spell checkers were primarily dictionary- and rule-based, later augmented with statistical language models and machine learning. Modern writing assistants (including current Grammarly features) increasingly combine traditional spelling and grammar checking with large language models. Leave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  5. Jul 22

    It's Not X, It's Y: Why AI Chatbots Pick Weird Favorite Phrases

    This week, Kimberly and Jessica dig into the AI writing tic everyone's noticed and nobody can fully explain: "it's not X, it's Y." They discuss The Atlantic's new piece on the phrase, then bring Kimberly's informal research from a publicly available corpus of 24-billion words of online news to show the construction is spiking right alongside "crucial" and "quietly." From there the conversation turns to what's actually at stake, including published work pulled from publication because it "sounded like AI," the argument that bad AI output is always a "you" problem, and the bigger question of who gets to decide what human writing is even supposed to sound like anymore. In this episode: The AI "tells" everyone's noticing, and the corpus data behind the hunchShakespeare, Vince Lombardi, and a DiGiorno ad — "it's not X, it's Y" is way older than any chatbotThe Atlantic's theories for why models love this constructionKimberly's own numbers: "not just X, but Y" is up 45% in online news since 2015AI as intermediary, not tool — and the Frontiers in Education paper that explains this"Don't judge the AI, judge the human" — and where that argument breaks downThe novel a publisher pulled over an AI accusationJessica's case that writing is thinking, and what's lost when we skip itPeach and PitLinksThe Most Famous AI Writing Tic Is Also the Most Mysterious — Will Oremus, The Atlantic, July 13, 2026. The article that kicks off the episode. NOW Corpus (News on the Web) — BYU’s large, continually updated news corpusTop 10 Most Common Words Used by AI — GPTZeroPublisher pulls horror novel “Shy Girl” over AI concerns — TechCrunchDefining and assessing AI literacy for researchers across the research lifecycle — Parker & Becker, Frontiers in EducationHow Not To Use AI — Abi Awomosu’s SubstackThe book itself — Abi AwomosuWomen Writin’ ’Bout AI — joint SubstackKimberly’s “Linguist in the Wild” Substack English with an Accent: Language, Ideology, and Discrimination in the United States — Rosina Lippi-GreenThe Design of Everyday Things — Don Norman, revised and expanded edition, MIT PressLeave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  6. Jul 15

    Remainder Humanism & Language Machines

    Every time a machine catches up to us, we redraw the line around what makes us human — and call whatever's left the "remainder." This week, Jessica and Kimberly (no guest) dig into Language Machines: Cultural AI and the End of Remainder Humanism by NYU professor Leif Weatherby, and ask whether the whole human-vs-machine contest is a trap. Along the way: why Emily Bender's "it's just intent" argument doesn't hold up as well as it seems to, why cognition and culture can't actually be separated, a 100-year-old linguistics theory (structuralism) that explains why LLMs work at all, and why the body — not the brain — might come first. In this episode The core argument — Remainder humanism: defining "human" as whatever's left over once machines take a skill. Why that's a losing game (the "arm wrestling a forklift" bit).Team Bender vs. Team Weatherby — Emily Bender's claim that intent is what separates human language from AI output, and Weatherby's counter: intent doesn't ground meaning, the language system grounds intent.Form vs. function — A quick linguistics 101 detour: language isn't just words on a page, it's what those words do in context ("it's hot in here" as a request, not a weather report).Cognition vs. culture — The WEIRD psychology problem (Western, Educated, Industrialized, Rich, Democratic) and why decades of "universal" cognitive science findings didn't hold up outside that narrow sample.Structuralism, 100 years early — The idea that words get meaning from their relationships to other words, not from pointing at things in the world — and why that theory basically predicted LLMs.Meaning without truth — Why hallucinations are what a meaning-making system with no truth-tracking looks like.Embodiment — Descartes' "I think therefore I am" flipped: feeling comes before thinking, and what that means for machines that don't have bodies.Practical takeaway — How to stop playing defense: quit asking "what can I still do that machines can't," start asking what these systems are trained on, who's represented, and who gets to shape them. Mentioned in this episode Language Machines: Cultural AI and the End of Remainder Humanism — Leif WeatherbyEmily Bender — linguist, "stochastic parrots" / text extrusionNoam Chomsky — universal grammar, cognition as separate from cultureMaha Bali — "Where Are the Crescents in AI?"Michael Pollan & Annika Harris — on consciousness and embodiment Personal segment The episode closes with a quick peach-and-pit check-in — home renovation surprises and a therapy update on sitting with feelings in the body instead of just thinking through them. Leave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  7. Jul 8 ·  Bonus

    Mothering the Machine: Feminist Theory Meets Silicon Valley's Newest Metaphor

    Guest: Dr. Michelle Morkert — gender scholar, leadership coach, founder of the Women's Leadership Collective. In this repisode (get it? re-episode?), Kimberly and Jessica sit down with Dr. Michelle Morkert to unpack the growing call from AI leaders for "maternal AI," which is the idea that treating AI systems like children we're raising will make them safer, kinder, and less likely to turn on us. Michelle walks through the difference between a gender analysis (counting heads) and a feminist analysis (asking who holds power and why), then the conversation turns to why "maternal" is a loaded, historically fraught word to hand to an industry that has never asked mothers what they actually need or wondered how mothers (or even women in general) might benefit. Topics covered: Gender analysis vs. feminist/intersectional analysis, illustrated through the demographics of the U.S. SenateThe "maternal AI" proposal from figures like Geoffrey Hinton (computer scientist and cognitive psychologist often referred to as the "Godfather of AI" and Mo Gawdat, former chief business officer at Google X. We talk about why they never get specific about what "maternal" would actually mean in practice. Sarah Ruddick's concept of "maternal thinking" as a non-gendered ethical stance, and how it differs from what's being proposed nowWhy Sam Altman's comment that saying "please" and "thank you" to ChatGPT costs OpenAI "tens of millions of dollars," which he called "well spent", is a small but telling data point in this conversation Deepfake harm and non-consensual imagery as the more urgent, material issue getting sidelined by the "maternal AI" metaphorRadicalization pipelines and the "tradwife" aesthetic as a case study in how "maternal" framing gets co-opted politicallyDonna Haraway's "God trick" and why tech's claim to neutrality keeps women out of the roomKaren Hao's Empire of AI and the Indigenous-language-model counterexample as a picture of what reciprocal, non-extractive AI development could actually look likeAlso referenced in this episode: Alison Gopnik, The Scientist in the CribLaura Bates on BBC's Radical with Amal Rajan (dehumanization and algorithmic feeds)Allie K Miller's interview on the Mel Robbins PodcastLeave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

  8. Jul 2 ·  Bonus

    AI Data Centers Are Coming to Your Backyard

    AI doesn’t live in “the cloud.” It lives in buildings: large, energy-hungry, water-dependent facilities that require land, cooling systems, backup power, utility agreements, zoning decisions, and public infrastructure. We’re re-releasing this conversation because the issue has become urgently local. Across the United States, communities are debating whether proposed data centers are good economic development, risky infrastructure bets, or something in between. Here in Ames, Iowa, the City Council is reviewing a proposed data center. The City of Ames says the proposal is still in the early review stage, with no final decision made, and that the full buildout could require up to 25 megawatts of electricity. Kimberly recently wrote an open letter to the Ames Mayor and City Council asking them to slow down, require independent review, and make sure ratepayers are protected before any binding commitments are made. Read it here: “Open Letter to the Ames Mayor & City Council: Re: Proposed Lightedge Data Center on Aviation Way.” This episode originally focused on 3 of AI’s environmental impacts, energy consumption, water use, and e-waste. But the larger question is civic: who pays for the infrastructure behind AI, who benefits from it, and who gets a say before it shows up in their community? Kimberly and Jessica talk with Jon Ippolito and Joline Blais about the physical infrastructure behind AI and the local consequences of the data-center boom. We discuss: Why AI is not abstract, weightless, or magically floating in “the cloud”What data centers are and why they require so much electricity, cooling, and landThe difference between individual AI use and concentrated industrial infrastructureWhy “innovation” can become a rhetorical wrapper for public risk and private profitHow data centers can affect utility planning, municipal water systems, noise, land use, and local tax policyWhy communities should ask hard questions before approving long-term leases, incentives, or infrastructure commitmentsThe Lewiston, Maine, data-center fight and what other communities can learn from itWhy “AI infrastructure” is not just a tech issue, but a local governance issueData-center debates are spreading across the country.  The National Conference of State Legislatures reported on July 1, 2026, that lawmakers in 15 states are considering bans or pauses on new data-center development while they study community impacts, grid resilience, and local costs. And nationally, more than 500 organizations from 47 states have called for a moratorium on new AI data centers until stronger protections are in place around energy, water, pollution, electricity rates, and community impacts. Kimberly’s open letter argues that the Council should require independent review before making commitments around a lease, sale, rate classification, or incentive package. The letter specifically asks the Council to protect current utility customers, evaluate the proposal against Ames’ climate and planning commitments, and require evidence around jobs, tax revenue, and community benefit before moving forward. Key questions for any community facing a data center proposal Before a city approves a data center, residents can ask: How much electricity will it use at each phase of development? Not just at opening, but at full buildout.Who pays for grid upgrades, substations, transmission lines, and backup infrastructure? If the answer is “the utility,” ask whether that means current ratepayers.How much water will it use, and what kind of water? Municipal drinking water, industrial water, reclaimed water, or something else?What happens during peak heat, drought, or grid stress? Data centers may look different on an average day than they do during peak demand.How many permanent local jobs will actually be created? Construction jobs are not the same as long-term local employment.What tax incentives, abatements, or special rates are being offered? Public benefit should be measured against public cost.What protections are binding? Promises in presentations are not the same as enforceable agreements.What happens if the company leaves, expands, sells, or changes use? Communities need to think beyond the ribbon-cutting.How does this project fit with the city’s climate, land-use, and economic-development plans? If a city wrote those plans, this is the moment to use them. Otherwise, congratulations, we invented decorative planning documents.Who gets to decide? Public land, public utilities, and long-term infrastructure commitments deserve public scrutiny.Related reading and resources City of Ames page on proposed Lightedge data center https://www.cityofames.org/News-articles/City-Council-to-Review-Proposed-Data-Center-Includes-Public-Input-ProcessIowa State Daily coverage of Ames City Council data center discussion https://iowastatedaily.com/339765/city-of-ames/city-council-discusses-data-center-proposition/NCSL: Which States Are Banning Data Centers? https://www.ncsl.org/fiscal/which-states-are-banning-data-centersAxios Indianapolis: Proposed data center rules move forward amid protest https://www.axios.com/local/indianapolis/2026/07/01/data-center-rules-vote-protestAxios Cleveland: Cleveland pumps the brakes on data centers https://www.axios.com/local/cleveland/2026/06/29/cleveland-data-center-moratoriumBusiness Insider: AI data center fight over Colorado River water https://www.businessinsider.com/ai-data-center-lawsuit-california-imperial-valley-colorado-river-water-2026-6Food & Water Watch: 500+ groups call for nationwide AI data center moratorium https://www.foodandwaterwatch.org/2026/06/11/500-groups-from-47-states-call-for-nationwide-ai-data-center-moratorium/Small Bottle, Big Pipe: Data centers and public water-system capacity https://arxiv.org/abs/2603.02705Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers https://arxiv.org/abs/2606.05420Search terms AI data centers, data center water use, data center electricity use, data center zoning, AI environmental impact, AI infrastructure, Ames Iowa data center, Lightedge Ames data center, data center moratorium, data center tax incentives, data centers and public utilities, artificial intelligence infrastructure, data center local impact Listen for ... The most important shift in this conversation is from abstraction to infrastructure. AI is often sold as software, intelligence, productivity, creativity, automation, or innovation. But data centers reveal something much more concrete: land, water, power, money, regulation, and political choice. That is where communities still have leverage. And that is why the question is no longer just “Should we use AI?” It is also: Should our town subsidize, host, power, cool, and normalize the infrastructure behind it? Leave us a comment or a suggestion! Support the show Contact us: https://www.womentalkinboutai.com/

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Ratings & Reviews

4.8
out of 5
11 Ratings

About

Two women examining AI through a lens of power, not just capability. Why deepfakes target women. How bias gets baked in. What tech companies aren't saying. Kimberly brings corpus linguistics; Jessica brings strategy. Both bring skepticism, feminism, research expertise, and a refusal to take the hype at face value.Subscribe to our channel if you’re also interested in understanding AI behind the headlines. 

Information

  • Creator
    Kimberly Becker & Jessica Parker
  • Years Active
    2024 - 2026
  • Episodes
    63
  • Rating
    Explicit
  • Copyright
    © 2026 Women talkin' 'bout AI
  • Show Website
    Women talkin' 'bout AI

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