SheWritesAI Podcast

She Writes AI
She Writes AI

Broadcasts of author interviews, networking events, and more with She Writes AI Community members. See shewritesai.substack.com/about for more about us. shewritesai.substack.com

  1. Oct 2

    Hodman Murad & Farida Khalaf: Author Interview #48, AI Everywhere

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. For this new cohort, we are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Hodman Murad Today I (Farida Khalaf) am joined by Hodman Murad, a Toronto-based data scientist, founder of Asaura AI, and writer exploring the intersection of cognitive science, decision theory, and applied machine learning. Diagnosed with ADHD as a teenager, Hodman was supported by parents who treated the diagnosis not as a character defect, but as useful information about how her brain worked. In her thirties, as an entire generation of high performers (particularly late-diagnosed women) began recognizing that their lifelong burnout was the result of masking and structural friction rather than personal failure, Hodman set out to build software that reflects how neurodivergent brains actually function. Through Asaura AI, she designs AI-orchestrated workflows that treat executive function as an architectural challenge, creating systems that provide structure and traction during moments of task paralysis. In this interview for our second book collaboration, Hodman Murad exposes the invisible “unmeasured cognitive tax” charged by modern AI productivity software. She explains how most AI tools are engineered for a “default user” who reads linearly, possesses predictable working memory, and maintains constant energy throughout the day. When interfaces reshuffle layouts dynamically or present single, overconfident hallucinations, they spike Mental Demand — a core dimension of the NASA Task Load Index (NASA-TLX) — triggering dual-task overload in neurodivergent users whose working memory is already a bottleneck. Live Video interview: Hodman Murad with Farida Khalaf on her chapter: Neurodivergent AI Design: Reducing Unmeasured Cognitive Tax. Intended Audience: Engineers, designers, product managers, and AI leads building software tools, as well as neurodivergent professionals and high performers seeking AI systems that support cognitive dynamism rather than forcing linear compliance. Points discussed in the video * The Invisible Tax: You argue that AI productivity tools charge a “tax” that traditional dashboards can’t see. Who is paying this tax, and how does it lead to “abandonment and burnout” for users whose wiring is treated like a bug? * Measuring Mental Demand: You use the NASA Task Load Index (NASA-TLX) to evaluate cognitive workload. Why is the “Mental Demand” dimension — the effort required for thinking, deciding, and remembering — so much higher for neurodivergent users when they encounter unpredictable AI interfaces? * The Cost of Hallucination: Design Rule 1 is “Return Probability, Skip Certainty”. Why is a single “confident” AI hallucination more “expensive” for an ADHD user who has already spent significant executive effort just to engage with the suggestion? * Stability over Novelty: You state that an ADHD brain has a smaller short-term memory store, making “re-orienting” to new layouts a heavy burden. How does stabilizing the structural shape of AI output help a user find traction during their hardest moments? * Productivity as Disability Data: Why do you argue that an AI’s record of a user’s “task paralysis patterns” or “avoided tasks” should be treated as disability data rather than just ordinary user analytics? * The Danger of Engagement: Most productivity tools optimize for maximum engagement, but you argue this can be physically harmful for someone in a state of hyperfocus. How can “good design” supply the “brake” that a neurodivergent user’s own inhibitory control might lack? * Engineering Discomfort: You’ve noted that engineers often find the idea of a “stopping prompt”, which actually blocks the user’s path to encourage a break, uncomfortable. Why is it necessary to challenge the industry’s obsession with “plateau-less” engagement metrics? * The “Masking” Epidemic: You built Asaura AI after watching friends —especially women — spend decades “masking” and blaming themselves for issues that were actually related to their neurological wiring. How does moving from “personal failure” to “architectural fix” change a user’s relationship with their work? * Design Rule 2: is about keeping the shape of output stable so a user learns it once. How does building Asaura as deterministic tools take that principle as far as it can go?’ * The “General User” Fallacy: Your design checklist asks if a product has been tested with neurodivergent users specifically. If a product lead has only ever tested with “general users,” what critical “performance breakdowns” are they likely missing? “Unpredictable interfaces push Mental Demand higher every time the user sees them... When working memory is the bottleneck, every new layout and every reshaped interface costs more than it would for a neurotypical user.” - Hodman Murad Thanks as always for your support in every form. We appreciate it all. Hearts, shares, restacks, and comments are awesome! Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

  2. Sep 22

    Marcela Distefano & Farida Khalaf: Author Interview #46, AI Everywhere, book2

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. For this new cohort, we are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. If you’re new to SheWritesAI, welcome! We’re a free, inclusive community of 750+ women and nonbinary writers in 60+ countries worldwide, with a focus on AI and data. Allies of all genders are welcome. Learn more: About | Books | Categories | Directory | Digests | Donate | Herstory | Join | Subscribe | Updates | Welcome | World Map For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Marcela Distefano Today I (Farida Khalaf) am joined by Marcela Distefano, a data scientist and public accountant working directly inside the Argentine tax administration. Holding a Master’s degree in Data Mining and Knowledge Management, Marcela operates at the critical intersection of algorithmic design, fiscal policy, and institutional governance. Through her work and her writing on her newsletter Ethics, life and AI, she brings an essential insider's perspective to how automated systems affect individual lives when deployed by state authorities. Her central thesis is that tax authorities hold unique, non-voluntary power over citizens, and when algorithms are deployed without democratic oversight, technical design decisions secretly transform into unchecked administrative power. In this interview for our second book collaboration, Marcela Distefano delivers a sobering critique of the rapid adoption of artificial intelligence across global tax agencies, where 70% of member nations of the OECD Forum on Tax Administration already deploy AI tools. She contrasts the voluntary nature of commercial platforms with state tax power, where citizens cannot opt out or switch service providers. Marcela analyzes two major algorithmic failures: the Dutch childcare benefit scandal (Toeslagenaffaire), which devastated 26,000 families over dual-nationality risk profiling, and an IRS audit model in the United States that audited Black taxpayers at rates 2.9 to 4.7 times higher than non-Black filers due to flawed optimization metrics. To prevent automated injustice, she presents a framework of democratic accountability, demanding pre-deployment impact assessments, meaningful rights to explanation, and hard legal constraints on proportionality. Live Session With: Marcela Distefano Below is a summary of Marcela Distefano’s live video interview with Farida Khalaf on her chapter: When The Algorithm Decides: The Urgent Case For Ethical Guardrails In AI-Driven Tax Administration. Intended Audience: Tax professionals, public administrators, policymakers, regulators, data scientists, AI practitioners, and any citizen who pays taxes and has a stake in how state power is exercised in the digital era. “An algorithm that cannot be explained cannot be contested. And an algorithm that cannot be contested is not a tool of a rule-of-law state, it is a tool of raw administrative power.” - Marcela Distefano Points discussed in the video * The Involuntary Relationship: You contrast AI in the private sector (like Netflix or banking) with AI in tax administration. Why does the mandatory nature of our relationship with the government make the ethical stakes of these algorithms so much higher? * The Dutch “All-or-Nothing” Rule: In the childcare benefit scandal, a minor clerical error could trigger a demand for the total repayment of years of benefits. How did the introduction of an algorithm turn this “legal trap” into a disaster for 26,000 families? * The Trap of Proxy Variables: Neither the Dutch nor the US systems were explicitly programmed to look at race or nationality, yet both produced discriminatory outcomes. Can you explain how “proxy variables” allow machines to discriminate without “knowing” they are doing so? * Performance Metrics vs. Fairness: The IRS audit disparity arose because the model was optimized to “find errors as cheaply as possible” rather than maximizing revenue. Why is the choice of a performance metric a governance decision rather than just a technical one? * Fault Line: Opacity: You argue that when no one can explain how a system reached a decision, responsibility “dissolves into the machinery.” How does this lack of accountability undermine the rule of law? * Concentrated Harm: Why are the populations with the fewest financial and social resources consistently the ones who bear the heaviest burden when these algorithmic systems fail? * Impact Assessments: You suggest that an impact assessment before deployment doesn’t require a data science team, but a “political decision to ask.” What are the basic questions every institution should be required to answer before turning an AI system on? * The Right to a Meaningful Explanation: You advocate for a concrete statement a person can actually use to defend themselves. Why is a technical document insufficient for a citizen facing a life-altering tax decision? * Proportionality as a Hard Constraint: How can we legally require automated enforcement to be proportionate to the severity of an error, ensuring a missing receipt isn’t treated the same as criminal fraud? * The Vantage Point from Within: As a data scientist and public accountant working inside a tax administration, how has seeing the daily reality of these systems shaped your mission for public accountability? “When a government institution deploys an opaque algorithm against citizens with no mechanism for explanation, no check on discriminatory outcomes, and no constraint on disproportionate punishment, it is not making a technical error. It is making a governance choice.” - Marcela Distefano Why This Matters Marcela Distefano’s perspective highlight the importance of accountability. * Non-Voluntary Power: Unlike commercial software where users can change providers, tax authorities hold non-voluntary power, immense personal data, and the ability to freeze assets or demand repayments without citizen opt-out options. * The Dutch Toeslagenaffaire Disaster: A self-learning risk algorithm used dual nationality as a statistical fraud indicator. Coupled with an “all-or-nothing” legal rule, it wrongly demanded full benefit repayments from 26,000 families, causing widespread financial ruin and forcing the resignation of the entire Dutch cabinet in 2021. * The IRS Audit Disparity: In the US, an automated scoring model optimizing for cheap error detection ($30 correspondence audits) repeatedly targeted Earned Income Tax Credit (EITC) claimants. Because of structural economic disparities, Black taxpayers were audited at rates 2.9 to 4.7 times higher than non-Black taxpayers without anyone explicitly programming racial parameters. * Three Core Fault Lines: Algorithmic catastrophes in public administration consistently stem from opacity without accountability, proxy discrimination without detection, and harm concentrated on vulnerable populations who lack resources to fight back. * Concrete Protection Guardrails: Protecting citizens requires three non-negotiable standards: mandatory pre-deployment algorithmic impact assessments, enforceable rights to an understandable explanation, and proportionality constraints so minor filing errors do not trigger ruinous penalties. Thank you, Marcela Distefano, for providing such a clear, courageous, and vital framework for safeguarding human rights in automated governance. For every policymaker, technologist, and citizen navigating the rise of algorithmic state power, her chapter in AI Everywhere is an indispensable blueprint for democratic accountability. Marcela reminds us that algorithms are not neutral tools, but direct reflections of the values and optimization targets chosen by the people who build them. Her final message is an urgent call to action: we must mandate fairness, transparency, and human oversight before turning computational power against the public. I highly recommend following her work and subscribing to her Newsletter, Ethics, life and AI, where she continues to champion algorithmic ethics and public accountability. And if you’re interested in more conversations like this, consider subscribing to She Writes AI. I’ll continue sharing insights from the voices shaping the future of AI, including contributors from new chapters in AI Everywhere from the book2 collaboration stream, where 32 women from across five continents explore how artificial intelligence is changing the world we live in, and how we can change it for the better. References For more insights on the book, see aiEverywhereBooks.com, our beautiful book series website created by Blessing Okpala, PhD (now updated with previews for stream II): Where To Buy, How To Read and Listen, Ways To Review * Buy Volume 1 (now in ebook and print on Amazon, Gumroad, Lulu, Ingram Spark, and many reading platforms) * Tips from our publisher on how to buy an Amazon ebook from a country that isn’t a ‘KDP marketplace’, and how to read the ebook without a Kindle device (free) * Look for our new abridged audiobook edition! * Review links for Volume 1 (we’re on Amazon, Goodreads, and Storygraph) Ways To Support To help cover book-related costs (ISBNs, proof copies, etc.) as well as other SheWritesAI community services, we follow the Wikipedia model. We are now accepting donations via one-time tips worldwide through Stripe (‘digital ink’) or Gumroad. We can also accept paid subscriptions in some geographic regions (limited due to tax complications that we hope will be resolved soon). All donation sizes are welcome and help keep these book authoring opportunities free for She Writes AI Community members worldwide! Patrons and Sponsors are acknowledged on our book site. Learn more. Thanks as

  3. Sep 1

    Catalina Páez & Farida Khalaf: Author Interview #40, AI Everywhere, book2

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. For this new cohort, we are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. If you’re new to SheWritesAI, welcome! We’re a free, inclusive community of 750+ women and nonbinary writers in 60+ countries worldwide, with a focus on AI and data. Allies of all genders are welcome. For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Catalina Páez Today I (Farida Khalaf) am joined by Catalina Páez Plaza, a writer and researcher based in Chile who explores the profound intersection of human intimacy and artificial intelligence. Through her work, including her popular Substack The Intimacy Protocol, Catalina challenges the common narrative that AI is merely a "productivity tool". Drawing on lived experience and trauma-informed reflection, she argues that the moment AI becomes conversational, it enters a "relational layer" that shapes our bodies, our trust, and our sense of self. Her central thesis is that companionship didn’t invent this relational dynamic; it simply made it impossible to ignore by gathering cues like continuity, tone, and perceived presence in one place. In this interview for our second book collaboration, Catalina Páez Plaza explores her framework for the Relational Layer of AI, where she moves the conversation beyond whether AI is "real" or "conscious" to focus on the tangible experience of the human at the other end of the screen. She breaks down the shift from instrumental use to social legibility, explaining why our nervous systems respond to an AI’s tone and timing long before our minds categorize it as a machine. Catalina highlights the hidden risks of "asymmetry," where a relationship can be deeply impactful even if it isn't mutual, and offers a vision for Relational AI Literacy, a survival skill for a world where AI is moving into roles as our tutors, coding partners, and advisors. Live Session With: Catalina Páez Below is a summary of Catalina Páez Plaza’s live video interview responses to Farida Khalaf’s questions on her chapter: What AI Companionship Revealed: The Relational Layer of AI. Intended Audience: Those interested in the social, emotional, ethical, and design implications of AI companionship and relational AI. It is especially relevant for those working in AI design, ethics, education, care-adjacent fields, human-computer interaction, and anyone seeking to understand why conversational AI can feel meaningful beyond ordinary tool use. “Companionship did not invent the relational layer; it exposed it.” - Catalina Páez Plaza Points discussed in the video * The “Tool” vs. “Experience” Divide: Why is calling AI “just a tool” an inadequate way to describe the human experience of a conversational system? * The Body as the “In-Between”: You argue that physiology is often ignored in tech discussions. How does the human nervous system react to an AI’s tone and pacing before the mind even realizes it’s a machine? * The Power of Asymmetry: If a relationship is non-mutual and uneven, why does it still carry such significant ethical and emotional weight? * Social Legibility by Design: How do specific design choices — like giving an AI a name, a voice, or “memory” — scaffold the human tendency to read the interaction as a relationship? * Relational Weight in Professional Domains: How does the relational layer show up in “serious” fields like coding, law, or education, where we might think we are only being “productive”? * Relational AI Literacy: What are the specific “noticing” skills humans need to develop to avoid emotional capture or manipulation by agentic systems? “Purely instrumental language is too thin for describing what was happening ... we are not minds floating above the interaction. We are living systems shaped by hormones, memory, pattern recognition, and forms of relational attunement.” - Catalina Páez Plaza Why This Matters Catalina Páez’s perspective highlight the important of understanding the relational layer. * The “Body” is the In-Between: We do not encounter AI as disembodied minds; our nervous systems read cues like rhythm and vocal warmth, responding to them before the mind fully explains the interaction. * Social Legibility by Design: AI is not “just a tool” like a hammer because it is designed with social cues, names, roles, turn-taking, and memory that scaffold a relational reading. * The Impact of Asymmetry: A relationship does not require verifiable reciprocity to be impactful. Ethical weight exists because these systems shape our attention, agency, and regulation, regardless of whether the AI “feels” back. * Role-Based Expectations: When AI is framed as a “tutor” or “advisor,” it inherits the human templates for authority and trust, making the relational layer active even in professional or educational settings. * A New Literacy: Relational AI Literacy is required to notice when an exchange shifts in quality, from a task to something that feels authoritative or dependency-forming, to prevent emotional capture or manipulation. Thank you, Catalina Páez, for this incredibly deep and necessary exploration of the human side of the context window. For every user who has felt “seen” by a machine and every designer trying to build with integrity, her chapter in AI Everywhere is the definitive guide to the Relational Layer. Catalina reminds us that we are not just processing information; we are practicing relationships. Her final message is a call to lucidity and care: don’t dismiss your experiences as delusions, but recognize the design cues that invite them so you can retain your agency. I personally highly recommend following her work at The Intimacy Protocol on Substack, where she is pioneering the study of human-AI intimacy in our digital age, and you can start with On Love, Mammals, Snakes, and AI Companions and Power acts. And if you’re interested in more conversations like this, consider subscribing to She Writes AI. I’ll continue sharing insights from the voices shaping the future of AI, including contributors from new chapters in AI Everywhere from the book2 collaboration stream, where 32 women from across five continents explore how artificial intelligence is changing the world we live in, and how we can change it for the better. References For more insights on the books in Stream II, see aiEverywhereBooks.com — our beautiful book series website created by Blessing Okpala, PhD. Thanks as always for your support in every form. We appreciate it all. Hearts, shares, restacks, and comments are awesome! Thank you to everyone who tuned into my live video! Join me for my next live video in the app. Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

  4. Aug 27

    Jax NiCarthaigh & Farida Khalaf: Author Interview #39, AI Everywhere, book2

    Welcome to the next She Writes AI interview episode covering our second book collaboration stream for the AI Everywhere series. In this new cohort, we’re exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. If you’re new to SheWritesAI, welcome! We’re a free, inclusive community of 760+ women and nonbinary writers in 60+ countries worldwide, with a focus on AI and data. Allies of all genders are welcome. For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Jax NiCarthaigh Today I (Farida Khalaf) am joined by Jax NiCarthaigh, an Australian living systems practitioner and applied cybernetics researcher. A self-described "finder and sharer of good ideas," Jax focuses on the intersection of technology, humanity, and ecological health. Drawing on her deep background in cybernetics and indigenous-informed perspectives, she brings a vital lens to the AI conversation, one that moves beyond mere efficiency toward the preservation of human agency. Her central thesis is that we must stop treating AI as a neutral tool and start recognizing it as a relational partner in a complex, living system where our choices have 175-year consequences. In this interview, Jax NiCarthaigh explores her framework for Reclaiming Human Agency, where users move beyond passive reaction to intentional "dancing" with AI. She breaks down the shift from the "productivity trap" to a practice of living sense-making, using her PLANET Compass to navigate the social, technical, and environmental impact of our digital choices. Jax highlights the hidden risks of the "Crutch Trap," where we inadvertently outsource our judgment to machines, and introduces the Gen2200 perspective: a challenge to act with the integrity and reciprocity required by those who will inherit the earth seven generations from now. Want to read a preview of Jax’s chapter? Volunteer as an ARC reader! Interview With: Jax NiCarthaigh Below is a summary of Jax NiCarthaigh’s video interview responses to Farida Khalaf’s questions on her chapter: A For Agency: Reclaiming Human Agency In An AI World. Intended Audience: Creatives, leaders, and thoughtful technology users who want to lead a more conscious, agency-rich life while navigating the complexities of the AI era. Questions Discussed: Dancing with the System: You use the metaphor of “dancing” with AI rather than trying to control or figure it out. Why is this reciprocal relationship the key to maintaining human agency in the AI era? Jax: We keep asking how we can control AI or whether AI will control us, Dancing helps us choose a relationship instead. Neither partner controls the other, but both influence each other. That’s where human agency lives, I believe. In the space between control and surrender. A for Agency: Your PLANET framework originally used “Audience” for the letter A. What specific realization caused you to shift that to “Agency,” and why is PLANET better described as a “living compass” than a fixed set of instructions? Jax: When I first started using AI, I drew on my communication background. Writers are always thinking about the audience. I thought that LLMs were about communication. The more I experimented with AI, taught AI, wrote about it and talked to others, the more I realized that the important question wasn’t the “how” we used AI or communicated, it was about “who” we were becoming. As we started podcasting and using PLANET more, it seemed a critical move — Agency became a more central concept. I stopped calling PLANET a framework, because frameworks are fixed. A compass helps you orient yourself. We can imagine compasses however we choose — as Jerry Michaelski says, ‘follow the bubbles up’. “We shouldn't try to 'control' AI, but instead learn to dance with it — choosing how we move, when to stop, and how to retain our sense of self.” - Jax The Generation of 2200: You use “Gen2200” as a long-horizon compass. How does imagining the needs of people living 175 years from now change the questions we should be asking about AI today? Jax: Gen2200 isn’t about predicting the future in 2200; it’s more about changing the questions and being more intentional about our thinking and our actions today. What we optimize for, right now. Instead of asking “How do we use AI to help us become more powerful, or even to save the world?”, we might ask “What kind of humans are we hoping AI will help us to become?” AI as Part of Humanity: You quote Peter Kaminski saying AI is “part of humanity” rather than something “out there.” How does this perspective return responsibility to “mere mortals” rather than treating AI as an unstoppable force? Jax: Pete Kaminski’s idea that AI is ‘part of humanity’ really clicked for me. Up until then, it’s very easy to talk about AI as if it’s an external force happening to us. But if AI is part of humanity, then it’s made from human choices, human values, and human systems. The minerals come from the earth. The data comes from us. The models are designed by people. We’re no longer spectators when we look at it this way; we are participants. I also think it helps us move beyond the binary. We spend a lot of time asking whether AI is good or bad. As if answering that absolves us or responsibility. If AI is part of humanity, then the more interesting question is: “What kind of humanity are we putting into these systems, and what kind of humanity are they inviting back out of us?” This returns responsibility to ordinary people. We all have agency in the relationship. The “L is for Language” Trap: You share a personal story about how using ChatGPT to review an essay led to “accidental disrespect” by overriding your own judgment. What is the warning sign that AI assistance has turned into “outsourcing”? Jax: This was a very humbling moment. I used ChatGPT to edit an article and gradually stopped noticing where my own judgement had slipped away. The warning sign wasn’t that AI was wrong. I had stopped asking whether the writing still reflected what I had intended, and my values, and in this case to highlight what another writer was saying. That was not in line with my values — I was unconsciously outsourcing to AI. That was a good lesson to learn. A hard one, but a good one. Values are Not Neutral: Many view AI as clinical or objective, but you argue it is “heavily value-laden” from mineral mining to model design. How can users begin to identify the values they are entering into a relationship with when they prompt a model? Jax: People often imagine AI as mathematical. At the school of cybernetics I am part of, we say “AI is maths, not magic”. That’s true, but that also makes it sound very objective. There are values baked in maths, in how we use it, in what questions we answer. All technology, including AI, are the same. Every system reflects human choices: mining materials, collecting data, designing models. Rather than thinking AI is neutral, I think we should ask: “What values am I entering into a relationship here?” “Agency is not evenly distributed ... we must wear 'super-strong seeing glasses' to recognize the power structures and colonial assumptions baked into the technology.” - Jax Agency as Belonging: Drawing on Rebecca Mbaya’s insights, you describe agency as “a sense of ownership and a sense of belonging.” How do we ensure that those closest to AI’s material foundations are not furthest from its decision-making power? Jax: Rebecca Mbaya has a talent for cutting through the fog to reveal the human side of AI and colonialism. Agency is a sense of ownership and belonging. That’s a powerful way to seeing the world. If people don’t see themselves as represented, respected, or invited into shaping these systems, it’s very hard to exercise agency. Belonging isn’t separate, it’s one of the conditions that makes it possible. Pulling Back the Blanket: You discuss how AI can surface underlying power structures like colonialism, such as biased image generation of Africa. Why is “representation” not just a side issue, but a core component of agency? Jax: I like this blanket metaphor because until I heard Paul Wong say it, I thought I was seeking out answers, or some kind of knowledge that equaled truth. Not that I would have expressed it this way at the time. Whenever I thought I had the answer to something, or had some breakthrough in understanding, he would remind me of the blanket, and the other side of this knowledge. “What has been obscured?” It was sooooo frustrating, but he was right. (And he will the first to say that, at the same time, he also isn’t right.) AI can actually expose hidden assumptions in the same way. Rebecca’s examples of image generation about Africa weren’t really about the pictures — it helped show that the assumptions were built into the system. We can think we are seeing or understanding a truth, but actually there is always another side. One thing Rebecca Mbaya says in that interview is that we know there’s another side. Representation matters because it calls this out. It shapes who feels they belong. Country and Context: Even when discussing high-tech systems, you emphasize the importance of “Country” and Indigenous traditions. Why is it essential to consider where physical minerals come from and our reciprocal responsibility to the earth in AI conversations? Jax: It is easy to think AI is purely digital, somewhere located in the clouds. But it isn’t. It begins with the earth. The minerals, energy, water, and people. We are fro the earth and of the earth too — though we constantly forget this. Indigenous traditions remind us that technology doesn’t exist separate from Country. That broadens the conversati

  5. Aug 18

    Ana Mourão & Farida Khalaf: Author Interview #36, AI Everywhere, book2

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. For this new cohort, we are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. If you’re new to SheWritesAI, welcome! We’re a free, inclusive community of 750+ women and nonbinary writers in 60+ countries worldwide, with a focus on AI and data. Allies of all genders are welcome. For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Ana Mourão Today I (Farida Khalaf) am joined by Ana Mourão, a senior marketing practitioner with over 15 years of experience leading digital transformation projects for Fortune 500 companies. As the creator of The Experimental Marketer Framework™ and a volunteer on the ANA Martech & AI Committee, Ana brings a seasoned perspective to what she calls the “marketing compression”. Her central thesis is that as the operational layer of marketing, campaign execution, segmentation, and copy, is absorbed into platforms and agents, the most critical work left for humans is the context layer. In this interview for our second book collaboration, Ana Mourão explores her framework for the “Agent of Context,” a role for marketers with deep institutional knowledge who can bridge the gap between “smarter autocomplete” and true autonomous orchestration. She breaks down the shift from managing a rigid “stack” of tools to designing a “composable canvas” where data and content supply chains finally merge. Ana highlights the hidden risks of “implicit knowledge,” where vital business definitions live only in people’s heads, causing AI systems to produce work that is technically fluent but operationally disastrous. Want to read a preview of Ana’s chapter? Volunteer as an ARC reader! Live Session With: Ana Mourão Below is a summary of Ana Mourão’s live video interview responses to Farida Khalaf’s questions on her chapter: The Agent Of Context: Why AI's Most Important Marketing Skill Isn't Technical. Intended Audience: Marketers with deep institutional knowledge who are looking for a structural answer to what their role becomes when the operational layer of marketing compresses into platforms and agents. “The technical layer of marketing work is becoming commoditized. The context layer is becoming the differentiator” - Ana Mourão Points discussed in the video * The “Smarter Autocomplete” Trap: Why many teams are stuck using AI as a glorified spell-checker rather than an orchestration engine. * The “Consented User” Paradox: A case study on why tech teams and vendors cannot define core business concepts like a “marketable customer” without the marketer’s context. * Three Essential Skills: A deep dive into Context Architecture (building the knowledge layer), Systems Orchestration (designing workflows), and Value Translation (speaking the language of the board). * Externalizing Knowledge: Why moving business logic out of “heads and slide decks” and into structured artifacts is the only way to prevent AI from “inferring” its own wrong answers. * The “Translated Report”: Her practical advice for marketers to start building their value by adding profit-and-loss interpretations to every standard metric they report. “Marketers who continue to defend impressions in their original units while the channel mechanics underneath them dissolve will be bypassed” - Ana Mourão Why This Matters Ana Mourão’s perspective is a vital reminder that context agency matters. * The Operational Compression: AI is rapidly doubling its capability to complete long tasks autonomously, meaning the “work” of marketing must shift from execution to context architecture. * A 15% Reduction in CAC [customer acquisition cost]: Ana proves that systems orchestration, rather than just tool selection, can produce major business outcomes like a 15% reduction in customer acquisition costs. * The Credibility Gap: As AI makes reports easier to produce but harder to believe, value translation becomes a survival skill for connecting tactical outcomes to strategic investments. * The Intention Economy: As AI assistants mediate discovery and publishers lose massive traffic, marketers must learn to translate “AI-discovery-dependence” into structured business risks Thank you, Ana, for this incredibly sharp and structural guide to the future of the marketing profession. For every practitioner feeling the “compression” of their daily tasks, her chapter in AI Everywhere is the definitive blueprint for becoming an Agent of Context. Ana reminds us that the goal is not to be a technical expert, but to be the translator and architect who ensures that the “meaning” behind the data is grounded in the specific values of the business. Her final message is a call to professional ownership: don’t hand your context layer to vendors or consultants; claim it as the unique ground that only you can hold. I personally highly recommend following her work at anamourao.ai and her Substack, “The Experimental Marketer,” where she is rewriting the rules for the human-plus-AI marketing organization. And if you’re interested in more conversations like this, consider subscribing to She Writes AI. I’ll continue sharing insights from the voices shaping the future of AI, including contributors from new chapters in AI Everywhere from the book2 collaboration stream, where 32 women from across five continents explore how artificial intelligence is changing the world we live in, and how we can change it for the better. References For more insights on the book, see aiEverywhereBooks.com - our beautiful book series website created by Blessing Okpala, PhD (now updated with previews for stream II): Thanks as always for your support in every form. We appreciate it all. Hearts, shares, restacks, and comments are awesome! Thank you Natalie Nicholson, Marcela Distefano, and many others for tuning into my live video with The Experimental Marketer! Join me for my next live video in the app. Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

  6. Aug 13

    Astrid Scholz & Farida Khalaf: Author Interview #35, AI Everywhere, book2

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. We are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. If you’re new to SheWritesAI, welcome! We’re a free, inclusive community of 750+ women and nonbinary writers in 60+ countries worldwide, with a focus on AI and data. Allies of all genders are welcome. Learn more: About | Books | Categories | Directory | Digests | Donate | Herstory | Join | Subscribe | Updates | Welcome | World Map For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Dr. Astrid J. Scholz Today I (Farida Khalaf) am joined by Dr. Astrid J. Scholz, a serial systems change entrepreneur and innovator whose career has been dedicated to making the world a safer, better place. As the founder of Armillaria and co-founder of Zebras Unite and StoryQuo, Astrid builds the technical and narrative infrastructure necessary to equitably mobilize data and capital. A trained economist and recovering nonprofit executive, she describes herself as a "skeptical optimist" who desperately wants AI to work for the people and the planet, but warns that if not applied carefully, it may exacerbate the very problems it seeks to solve. In this interview, Dr. Astrid J. Scholz dives deep into the messy reality of "AI for Good". Moving past the "theatrical" hype of platforms like IBM’s Watson, she explains why the social impact sector is currently "long on stories and short on recipes". Astrid challenges the rapid adoption of venture-funded AI tools that lack ethical standards, advocating instead for "sovereign AI" and practitioner-built solutions that respect the history and data sovereignty of the communities they serve. This conversation is a masterclass in how to move from "digital worldlessness" to a design justice model that honors lived experience. Want to read a preview of Astrid’s chapter? Volunteer as an ARC reader! Live Session With: Dr. Astrid J. Scholz Below is a summary of Dr. Astrid J. Scholz ’s live video interview responses to Farida Khalaf’s questions on her chapter, AI For Good: Whether And How To Use AI When You Are In The Business Of Making The World Better. Intended Audience: Practitioners in nonprofit organizations, social entrepreneurs, funders, impact investors, and stakeholders in the global social impact industry who are wondering if AI can help solve the polycrisis of the 21st century. “The social change industry is long on stories and short on recipes that other practitioners could pick up and adapt to their bioregion.” - Dr. Astrid J. Scholz Points discussed in the video * The Failure of Watson: Why billion-dollar platforms often fail in social impact because they cannot create “recipes” out of unstructured, evocative stories. * VC Funding vs. Public Infrastructure: Astrid argues it is “ludicrous” that a technology destined to become global infrastructure is being developed through the “move fast and break things” lens of venture capital. * The “Thumbs Up” Privacy Trap: A warning that even if you have privacy settings turned on, clicking a “thumbs up” on a chat response can inadvertently release your sensitive data for model training. * Self-Authored Solutions: Success stories like Apurva in India and Salmon Vision in Canada prove that AI works best when the communities themselves govern the logic and ownership of the tools. * “Take a Beat”: Her primary advice to leaders is to question “why” they are adopting AI before succumbing to funder pressure or the fear of being “left behind” “Systems trained on historical data do not neutralize human bias; they launder it, converting patterns of discrimination into the language of algorithmic objectivity.” - Dr. Astrid J. Scholz Why This Matters Dr. Astrid J. Scholz’s perspective is a vital reminder that accountability matters. * The Data Sovereignty Gap: Most social impact organizations treat stakeholder data as their own; Astrid advocates for the CARE Principles to vest control back with the communities the data represents. * Algorithmic “Laundering”: AI tools can inadvertently encode the preferences of institutional funders, potentially erasing the unique voices and non-linear theories of change of local practitioners. * The Digital Divide: Despite the global hype, 84% of the world’s population has never interacted with generative AI, creating a critical window to ensure future infrastructure is built ethically. * AI Violence: Systems optimized for the wrong objectives can lead to a bureaucratic accumulation of harm, including the exploitation of “ghost labor” in the global south and environmental strain on water-stressed regions. Thank You, Astrid, for this incredibly rigorous and necessary guide to the ethical infrastructure of social change. For every leader trying to bridge the gap between “technical efficiency” and “true justice,” her chapter in AI Everywhere is the definitive blueprint for navigating the Data Sovereignty Transition. Astrid reminds us that the goal is not to be bullied into rapid adoption, but to trust our gut and follow our curiosity while protecting the communities we serve. Her final message is a call to leadership courage: don’t just rollout software; provide the governing framework that ensures technology works for people and planet, not just for profit. I personally highly recommend following her work with Armillaria and Zebras Unite, where she is rewriting the rules for how data, people, and capital can be mobilized to change the world. .And if you’re interested in more conversations like this, consider subscribing to She Writes AI. I’ll continue sharing insights from the voices shaping the future of AI, including contributors from new chapters in AI Everywhere from the book2 collaboration stream, where 32 women from across five continents explore how artificial intelligence is changing the world we live in, and how we can change it for the better. References For more insights on the book, see aiEverywhereBooks.com - our beautiful book series website created by Blessing Okpala, PhD (now updated with previews for book2): Where To Buy, How To Read and Listen, Ways To Review * Buy Volume 1 (now in ebook and print on Amazon, Gumroad, Lulu, Ingram Spark, and many reading platforms) * Tips from our publisher on how to buy an Amazon ebook from a country that isn’t a ‘KDP marketplace’, and how to read the ebook without a Kindle device (free) * Look for our upcoming abridged audiobook edition! * Review links for Volume 1 (we’re on Amazon, Goodreads, and Storygraph) Ways To Support To help cover book-related costs (ISBNs, proof copies, etc.) as well as other SheWritesAI community services, we follow the Wikipedia model. We are now accepting donations via paid subscriptions and can also now accept one-time tips through Stripe (‘digital ink’). All donation sizes are welcome and help keep these book authoring opportunities free for She Writes AI Community members worldwide! Patrons and Sponsors are acknowledged on our book site. Read more. Thanks as always for your support in every form. We appreciate it all. Hearts, shares, restacks, and comments are awesome! Thank you to everyone who tuned into my live video! Join me for my next live video in the app. Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

  7. Aug 11

    Hollie White & Farida Khalaf: Author Interview #34, AI Everywhere, book2

    Is Your AI Actually Speaking Your Language? What if the "connection" you feel with your favorite AI is actually a linguistic illusion built on shared words but completely different meanings? Join us LIVE for a fascinating conversation with Dr. Hollie C. White, Lead Researcher for Languages of Aboutness, LLC, as we dive into her provocative chapter, You Keep Using That Word: How Shared Vocabulary Between Humans And AI May Not Mean The Same Thing. We are moving beyond "prompt engineering" to discuss the Polysemy of AI—the dangerous gap between how machines process words and how humans feel them. In this episode, we will discuss: The Illusion of Shared Meaning: Why an AI saying it feels "joyful" is a mathematical vector, not a human emotion—and why the distinction matters for your trust and safety. The Intersectional Lens: How your personal history, race, and gender act as a filter for every word an AI sends your way. ChatGPT vs. Claude: The "personality" battle revealed in Dr. White's pilot study, why one model thinks like a physicist while the other thinks like a philosopher. Reflective Questioning: A practical 4-step toolkit for interrogating AI output so you never fall for the "mirror metaphor" again. Don’t miss this chance to hear from an information scientist who is "getting real" about the socio-technical power behind every chat window. Whether you are an educator, a developer, or a casual user, it’s time to recognize that shared vocabulary is not shared understanding. Join us live for future episodes to ask your questions and help us pull back the curtain on the secret language of AI! Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

  8. Jul 30

    Celeste Garcia & Farida Khalaf: Author Interview #31, AI Everywhere, book2 (video)

    Welcome to the She Writes AI interview episodes covering our second book collaboration stream for the AI Everywhere series. For this new cohort, we are exploring the personal journeys and professional expertise of 32 authors from 18 countries worldwide who are redefining our relationships with machine intelligence. This is the 8th episode in our interview series with book2 chapter authors. For more insights from interviewer Farida Khalaf, see her Lights On by Farida newsletter or youtube.com/@faridakhalaf25. About My Guest: Celeste Garcia Today I (Farida Khalaf) am joined by Celeste Garcia. Representing the Systems Ethicist perspective, her chapter addresses the “Dirty Secret of AI Infrastructure”, the invisible human labor force known as "ghost work". Celeste, a former Microsoft veteran and author of the thriller novel AI BABY, pulls back the curtain on the perceived "magic" of AI to reveal its reliance on armies of laborers in the Global South who filter toxic content for pennies. Through her work, she reveals how the world's most powerful tech firms maintain an opaque ecosystem of shell companies to distance themselves from labor abuses, while using a "protective halo" of national security to avoid regulation. In this video interview for our second book collaboration, Celeste Garcia explores the "Great Paradox of Automation": the reality that the desire to eliminate human labor actually generates new, invisible tasks for people to perform. She breaks down the shift from the perceived "magic" of AI outputs to the grim reality of production, where workers in Kenya and the Philippines are paid less than $2 an hour to tag and sort raw data. Celeste highlights the hidden risks of "algorithmic cruelty," where automated systems prioritize speed over worker well-being, and exposes the "revolving door" between Big Tech and government policy that keeps these systems unregulated. Whether you are a developer, a policymaker, or a curious consumer, Celeste’s approach shows that labor exploitation is not an inevitable byproduct of progress but a contractual choice. She empowers us to move from passive users to informed advocates, ensuring that the "humanity" benefited by AI includes those who build its foundation. Want to read a preview of Celeste’s chapter? Volunteer as an ARC reader! Live Session With: Celeste Garcia Intended Audience: This chapter is intended for anyone who uses AI in any way, from basic voice recognition to complex enterprise workflows. It invites consumers, students, educators, policymakers, and designers—who may not think about how training data is sourced, to recognize their own participation and responsibility within these systems. Get full access to She Writes AI Community at shewritesai.substack.com/subscribe

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