AI
I attended the Clayman Institute’s talk on Gender, Power and Artificial Intelligence earlier this month. They now have the session shared on YouTube for posterity. There are many topics and ideas here to chew on as this AI moment develops, and I recommend giving their angles some consideration.
There are enough common signs of AI writing now that the subject has its own Wikipedia page. Via John Gallagher’s latest on “template rhetoric.”
The Pope provides a vision for living with artificial intelligence (gift link).
After listening to the talk yesterday, I was reminded of this article describing a journalism model for using AI that shared some parallels with D’Ignazio’s research.
Gender, Power and AI: Wrestling for the soul of the network, again
Thursday, May 14, 2026
Stanford’s Clayman Institute ran a virtual panel this morning called “Gender, Power, and Artificial Intelligence,” with Safiya Noble (UCLA), Catherine D’Ignazio (MIT), Angèle Christin (Stanford), and moderator Genevieve Smith, a Clayman Institute Postdoctoral Fellow. The panel applied principles from feminist tech studies to the current moment, and covered how gender norms get encoded in data and reproduced by AI systems, and discussed whether the technology has real capacity for equitable design and implementation at scale.
Noble’s argument throughout is that the governance conversation has gotten too high-level and universalizing while the actual outputs of these systems have profound day-to-day consequences for specific people today. She named the role of AI in the recent gerrymandering of Louisiana and Indiana as examples, and called for tripling down on long-term social science research about AI’s impacts. She also pointed out that philanthropy is retreating from feminist academic and organizational work because that work originates from the same dynamics that critique philanthropy itself, precisely at a point when this research is sorely needed. A lot of money is moving in AI, and very little of it is funding the people best positioned to study how it impacts everyone downstream.
D’Ignazio was asked directly whether feminist generative AI at scale is possible. Her answer was no, with caveats, given who owns the technology today and the current emphasis on profit motive. She suggested it is more important to consider how to organize around our relationship to technology, and how we might approach questions of profit and ownership, policy and decision-making, and data and tech governance.
She provided an example of a reasonable use case by walking us through a project from her Data + Feminism Lab. The example is documented at length in her recent book “Counting Feminicide: Data Feminism in Action,” where her team partnered with activists who scour news reports to document the gender-related killing of women and girls, including cisgender and transgender women. The lab built a very lightweight AI-based approach that streamlines the scanning and identification of news stories as possible cases to include in their project, supercharging their work (note: very similar to how the NYT uses AI to analyze data for reporting). In this example, the AI’s job is task-scoped, democratically co-determined with the people who use it, and small. Smith picked this up: there is an idea baked into the current LLM moment that AI must scale to make it marketable, and the alternative is using purpose-built models that are right-sized against a body of work.
Christin spoke at length about how embodiment is one of the primary focuses of feminist theory, and how AI perpetuates the “disembodied” illusion of technology, and how this dynamic shows up in everything from the marketing to UX to user comprehension. This spoke to my thoughts on how the single-interface design of LLM chat reproduces Haraway’s “god trick,” knowledge that presents as universal while concealing the specific and situated position it comes from.
The parallel I kept returning to, listening to this, is one I think about often with my own cohort of early bloggers, women who grew up alongside the rise of the internet — and then the rise of ad tech. The internet of the late 1990s and early 2000s was being shaped by several camps: writers, students, information architects, and user-centric researchers who saw it as an information access network and a space of possibility; entrepreneurs and opportunists who saw it as a channel for marketing, monetization and extraction; and a smaller boycott camp that wanted to limit and refuse the whole personal computing and digital revolution altogether.
It was generally considered weird to be a girl on a computer or a woman on the internet — so weird that many of our peers didn’t recognize us at all — and we were there anyway, making stuff, witnessing, learning, advocating, producing, influencing. So when I watch some of my old peers, many of whom are professional writers and academics today, treat LLMs as a question of refusal rather than a condition to engage with critically, I worry we are abdicating a responsibility at precisely the moment when our technical and rhetorical expertise applies. Their refusal has good logic: user-centric researchers and communities engaged extensively with the early internet and the extractive camp won anyway, so why expect a different outcome here?
But Noble’s work on algorithmic bias attributes that failure not to engagement, but to the institutional and financial disadvantages that user-centric approaches operated under relative to gargantuan commercial interests. David and Goliath. That gap does not close through abstention. Understanding the trade-offs around tech, producing knowledge and analysis that does not depend on investors and marketers to frame the platform and the questions, requires presence. Refusal cedes so much ground.
Overall, the recommendations from the panel were practical. Noble called for people with capital (and the political will to spend it) to consider how to put money toward socially responsible research and development. D’Ignazio called for alternative funding infrastructure outside of venture capital logic, and pointed at European digital sovereignty models as worthy of consideration here. She also gestured at the popular AI Skeptics reading group as one current example of mad-and-commiserating-as-organizing that is creating safe psychological space for people to talk about AI and its tradeoffs. Christin’s recommendation was community organizing, on the grounds that LLMs are unpopular with a lot of people who feel there is no space to say so, and that finding those spaces is itself worthy because it provides shared language and awareness of others’ knowledge and experiences.
Personally, it was refreshing to hear reflections on the work (and the feelings) of being inside institutions that are being reshaped by AI, and being responsible for some of how that reshaping gets communicated and absorbed. I’m thinking about the incredible value of interdisciplinary governance, and how the commitment to governance is a specific position, and all the margins to consider.
Further reading:
Catherine D’Ignazio and Lauren Klein, Data Feminism. The foundational text on applying intersectional feminist thinking to data science practice.
Catherine D’Ignazio, Counting Feminicide: Data Feminism in Action. Extended case study of the grassroots data activism project D’Ignazio described on the panel.
D’Ignazio et al., “Feminicide and Counterdata Production.” Research paper on the counterdata methodology behind the femicide tracking project.
D’Ignazio et al., “Data Feminism for AI.” Conference paper extending the data feminism framework to questions specific to AI systems.
Safiya Noble, Algorithms of Oppression. Noble’s study of how commercial search engines reinforce racism and sexism through their ranking systems.
Donna Haraway, “Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective” (1988). The original essay where Haraway introduces the god trick and the case for situated, embodied knowledge against the view from nowhere.
Reflections on teaching fiction writing in the age of AI, from a professor with ten years of classroom experience teaching writing at MIT.
It’s like the phrase “turtles all the way down,” but turtles are marketing.
A new group is attempting to map influence in the AI industry, with the goal to “produce a structured, shareable, and dynamic resource that identifies who is working on what, where the gaps are, and which partnerships might form across ideological and organizational lines.”
A new study suggests that people who use AI for writing are more able to detect AI writing than automated scanner tools. My current LLM pet peeve is how they use language like load-bearing, structural and legible to describe most ideas.
“Amazon has launched a new feature that uses AI to generate a short, podcast-like audio segment where two AI ‘hosts’ discuss the merits and reviews of a specific product.”
I’m reading about trends in book and phone bans in American public schools, and reminded that reading novels was once considered an idle and immoral pasttime, just as internet use is today. This 2016 article from JSTOR goes into the history of reading books and the fear that it “enfeebled the mind.”
Adventures in AI: I asked a Claude agent (new Opus, Pro plan) to build a Google Doc template with multiple tabs, using an existing doc as reference. It failed three times over two days, burned thru tokens, never worked with Drive. Eventually it spat out text for me to paste into a doc I made myself.
Here’s one way to use an AI summary function: stress testing opinion pieces against the facts. I ran the latest essay from local urban development curmudgeon Paul Fanlund through Claude and asked it to stress-test his arguments about ageism and city development in Madison, WI. Here’s what it says:
“On housing, Fanlund implies that development pressure is ideological overreach and that its critics skew old. Neither holds. Madison has a genuine supply problem — the city is targeting 15,000 new homes by 2030, and opposition to density isn’t age-sorted; it cuts across longtime homeowners who feel the planning process has been opaque.
“On streets, the column is flatly at odds with the evidence. Road diets reduce crash frequency and severity, and decrease pedestrian exposure to traffic. Older adults are among the most vulnerable pedestrians — street calming protects them more than it inconveniences them. Framing lane reductions as an anti-senior policy inverts what the safety data actually shows.
“There’s also a structural irony: Fanlund criticizes generational stereotyping while using “older residents” as shorthand for a specific political position on cars and density. That’s the same move, just pointed in a direction he agrees with.”
I served for two years on a traffic commission, and in my experience the civic tension between “road diets” and other road calming methods is real. The problem with advocating for traditional interventions like signage over modern interventions like road diets and roundabouts? Nobody listens to them.
NYT writes about the surge of “slopaganda.”
Fellow Madisonians, someone pulled together a website ranking local businesses in Madison by how local they are (by what criteria, idk). In my experience, this is one way we’re likely to see AI used in the next couple of years, via prototyping and/or executing ideas that result in dynamic websites.
Digital humility stitch: A new AI tool puts typos back in your emails, so they don’t look like they were written by AI.
Centaurs and Cyborgs on the Jagged Frontier by Ethan Mollick in 2023: “On some tasks AI is immensely powerful, and on others it fails completely or subtly. And, unless you use AI a lot, you won’t know which is which.”
Last night I had dinner with a friend in tech who recently attended a training on AI and analytics, where they made the observation that we’re in the “Napster era” of artificial intelligence. It’s an imperfect comparison but useful to consider.
Anecdotally, I’ve seen two family court cases where one party submitted full AI chats — prompts and colorful complaints included — as formal filings. The complaints wouldn’t pass muster with a real lawyer, but the conflict was nurtured by AI nonetheless. One was dinged for wasting the judge’s time.
I’ve posted a couple of times about instances I’m aware of where people are using AI in pro se court cases, especially family courts. A new study shows evidence of increasing numbers in pro se cases at the federal level, exacerbating existing bottlenecks. Many trade-offs abound here.
Timothy Chester offers some thoughts on the place of AI-assisted software development in a modern research university, and suggests that just because you can doesn’t necessarily mean you should.
Out: Twitter on a vape. In: AI-powered crypto vape. Is this real? Who can tell anymore.
Maris Kreizman on AI pressures building in the publishing industry, particularly how it impacts writers and editors: “It’s not an ideal environment for productivity, let alone for making art.”
Tom’s Hardware on Mythos and marketing hype. Additional commentary from Michael Corn, asking whether Mythos coverage reinforces or establishes perceptions about cybersecurity.
I once worked in a role where I keyed million dollar manufacturing orders into SAP, information that directly fed into factory specs for a manufacturing facility based in another country. Our regional office fed into a massive, global electrical engineering firm that ran on small margins (electricity delivery is a well-trod market), so our ability to deliver accurate orders on time was a differentiator in a field that is otherwise easily interrupted by chip shortages and logistics chains.
It was a big job. I learned a ton about electrical engineering, manufacturing and global logistics from a particular vantage point in North America. Our headquarters were based in Sweden, with locations around the world to support the electrical grid(s), covering both hardware and software solutions. My colleagues and I worked in positions that sat somewhere between B2B customer service, inside sales and data entry, and were expected to maintain a 99.8% accuracy rate because a single fat finger error would cascade across myriad systems, impacting real-world operations to the tune of hundreds of thousands of dollars per error.
Once (and only once), I fat-fingered a serial number during data entry which ruined an entire shipment of widgets. In response, the factory in Mexico sent the incorrect order of widgets, about five pallets, to my location in the United States so I could correct the order by hand. One by one, I had to physically remove each widget from a pallet, then from its individual shipping container, make a correction on the widget itself, and repackage each one, signing my name on each unit to ensure it was corrected by an accountable employee. I can’t recall why the issue couldn’t have been corrected on the factory floor, but it wasn’t on the menu. It was going to stay my problem.
This was the one and only factory error I made in about five years of tenure, precisely because it was so painful to correct it. The process was a little embarrassing but nobody made it especially so. Instead my coworkers up and down the org chart relayed a simple expectation: the desk workers need to pay attention to the details because the alternative is too costly. A few old-timers made sure to razz me about it in good humor, but ultimately the error was mine and the fix was mine, and the experience stuck because the whole chain of responsibility understood the stakes and reinforced the consequences. They also trusted me to stick around and continue to do my best.
During my annual review that year, I was dinged for only having a 98% accuracy rate, and I knew why that was a fair assessment.
I thought about this when I read today’s NYT piece on whether 90% accuracy is good enough for LLMs.
Related-ish: The important legacy of the Sarbanes-Oxley Act.
Poell argues AI is entangled with platform capitalism through shared infrastructure, reinforcing concentration of the market. The hype obscures local realities of adoption, putting public alternatives in the position of proving their existence alongside advocating for their place in the market.
Seeing a lot of discussion about trade-offs related to vibe-coding.
Angst about data center development continues to grow in the Rust Belt.