Grose covers how Scandinavians approach AI and ed tech by emphasizing peer review and removing the profit motive.
AI
To read: this report from Common Sense Media, “Talk, Trust and Trade-Offs: How and Why Teens Use AI Companions.” About a third of teens have turned to an AI for serious conversations about their own mental health and relationships, showing these tools are already embedded in teen social life.
Computer Love
I listened to two great podcasts this week exploring the tangle of human/AI relationships. In “Computer Love,” the guests explore whether these relationships are good for us, and in “Your AI Companion Doesn’t Love You,” they explore the technical and business implications of these relationships. Both of these feature podcaster and writer Bridget Todd, who is promoting her new book “Love at First Prompt.” As of today, about 1 in 5 young people in the U.S. have asked a chatbot for mental health advice, and 1 in 5 high schoolers have had a romantic AI relationship.
Note: Todd’s (excellent) podcast title is a reference to Rule 30 of the internet.
Chester argues that universities should govern AI by classifying data by sensitivity rather than approving specific tools. Public data goes anywhere –> regulated data requires an institutional contract –> sensitive data requires a local system. He also makes a renewed case for MS Copilot.
Gruber argues that AI text watermarking likely degrades prose quality and, even if it doesn’t, remains objectionable because it operates as an embargoed, unverifiable tag or signal controlled by providers like Anthropic and Google.
A person was found to be hiding an AI prompt injection in a legal filing that tells any AI that reads it to side with his perspective. The judge in this case likened it to “an automated agent communicat[ing] covertly with a juror during trial.”
Computah: Make it a link
I spent a little time with the blog last night and pulled together two new site features using Claude Cowork. The last time I experimented significantly with Claude like this was to use Claude chat to build the link log from scratch, walking it through my thinking in plain language, then copying and pasting its suggestions into the backend of the site and hitting publish. This time I used Cowork, the tool that runs in the browser, and it clicked through the screens itself, fully taking on the execution of tasks. I have some coding skills, but not the kind these changes required. If I were taking this on, I would need YouTube, Hugo for Dummies, and my own personal IT guy, and still probably couldn’t pull it together.
Last night I asked a few things of Claude: I asked Claude Cowork to get into the backend of the micro.blog site and change my theme to link each line in the linklog back to my original post. The date field in the right column now links back to the original post for each link logged. No problem, easy request with easy execution. I made the request, confirmed the plan, and went about my business while Claude made the edits.
I also asked it to analyze my post content and suggest category tags for groups of content, then to label that content correctly in the backend. Claude reviewed about 500 posts and suggested I add a few new categories to my blog: AI in Practice, Books & Reading and Writing & Language. Then I set up some auto-filters to run at publish time to automatically categorize posts based on keywords moving forward.
Finally, I manually added the archive page, which lets you sort posts by category or year. This means I now have a functional archive here. Enjoy my anodyne thoughts, dear reader.
Adjacent to my day job, I’ve been toying with Claude Pro now for about a year; in my experience, it has improved significantly within the last six months. It’s not perfect: my requested edits were completed, but it also changed the CSS on the linklog so some of the text is too light to read, which I didn’t ask for and don’t want. But it has arguably extended my ability to execute on work that requires skills I don’t otherwise have (such as design and coding). What I do bring to the table is an expansive practical background in publishing and production, and all the language to describe it.
Claude is launching an AI watermark, one that users can’t opt out of (but can easily outsmart). As internet god Notopoulos wrote, “Claude’s watermarking poses the question: Are you willing to proudly own that you write with AI?” More: What is an AI watermark and how do they work?
The knitting community is one of the great deliberative communities of the internet, and as they’ll happily tell you, knitting and fiber arts are closely tied to industrial and computing history. It’s really hard to pull one over on knitters online, because they have a deep and different relationship to the technical side of their hobby and all of the political and tech history behind it, and especially how it relates to computer engineering. I was thinking about that dynamic while reading about the latest fracas around YouTuber Hank Green, who is taking time away from public life after being called out by his audience, when his AI use was perceived as being in contradiction with his public persona. If you want to go down the rabbit hole on this, here’s a good video summary.
Ultimately viewers are reacting negatively to Green’s admission of using AI to quickly aggregate research to drive his content creation, suggesting that he lost his own creative discretion amid a growing demand for online content. But in the meantime, his technical inaccuracies were paired with completely avoidable gender bias alongside the AI use, which drew additional scrutiny from technical experts. It wasn’t his first go-round with the knitting community, it turns out. Was the bias Green’s or facilitated by his use of the tools? Debate ensues.
The Atlantic takes on the prevalence of the “it’s not X, it’s Y” construction in AI-generated writing, highlighting the semantic roles in sentence construction that differentiate ideas for clarity. Chatbots seem to like this construction, despite how clunky it sounds to the human ear.
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
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.
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.”
“A.I. is less regulated in America than sandwiches,” said Mr. Tegmark, who is also a physics professor at the Massachusetts Institute of Technology. “You can’t open a sandwich shop without having your kitchen inspected. But you can release an A.I. girlfriend for 11-year-olds and that’s fine.”
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.
Some initial review suggests that a staggering amount of Substack content is written by AI.
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.
A professor asked students to self-report AI usage on their homework, leading to lots of confusion and uproar. Points aside, it’s clear people want more clarity up front about when and whether to use LLM tools. In the meantime, treating students like they’re guilty until proven innocent is a bad MO.