Yesterday, I blogged (en français) on Jacques Ellul’s emphasis on the need for a technology-responsive ethic that emphasizes (among other values) tension and conflict. Ellul explores this ethic—one of non-power—in a few different writings that feel like different drafts of the same thing, and so I’ve seen that emphasis come up a few times as I’ve tried reading his work. Every time, it surprises me a little bit. Why, in articulating an ethical framework, would you emphasize tension and conflict?

Dealing with yet another case of “these reviewer suggestions are good and pretty straightforward, so why don’t I have any motivation to make the revisions?”

It feels like it’s Big Tech’s world and schools are just living in it.

Ça fait plusieurs mois que je m’intéresse aux écrits de Jacques Ellul comme base théorique pour comprendre les techniques et technologies de nos jours. En fait, j’ai déjà écrit en février au sujet de l’intelligence artificielle générative et combien l’œuvre d’Ellul semble utile pour les critiques de l’IA malgré le fait qu’Ellul a vécu et écrit bien avant l’ère de l’IA comme nous la connaissons aujourd’hui.

Je suis en train de lire son livre posthume Théologie et technique (bien lentement, il faut l’avouer—j’avais commencé le livre en mai avant de devoir recommencer il y a quelques jours), et je trouve qu’il y a plusieurs passages qui me semblent utile lors des débats actuels au sujet de l’IA générative.

Don’t know what to say here except that I don’t like any of this. Reminded of two arguments from Ellul:

• First, that an effective ethics of technology considers systematic effects, not “good” uses vs. “bad” uses,

• Second, that “because it exists” is not sufficient justification for adopting a technology.

Anyway, here’s the gift link.

More on why we need to talk epistemology when we talk generative AI:

Musk tweaking his AI model to be more aligned with right-wing edgelords was inevitable, but there’s a broader point to be made: each AI model is a black box that supposedly gives objective answers but in reality is shaped by its owners. As more people look to AI to learn about the world, the people who control how it’s trained and how it responds will control our prevailing narratives.

Voilà pourquoi il faut parler de la théorie de connaissance quand on parle de IA:

On est passé d’un moteur de recherche à un moteur de réponse. C’est-à-dire que les algorithmes proposent des versions rédigées à partir des données qu’ils auront collectées sur Internet, puis reformulées sans que vous ayez rendu visite aux sites contenant ces éléments de réponse à votre requête.

Si on doit absolument investir au niveau national dans l’IA, je préfère cette approche (investir dans les recherches).

J’aimerais parler de ce sujet à mes étudiants. Même aux États-Unis, on doit nous demander de qui nous sommes dépendants dans notre vie numérique.

Hadn’t heard about this, and it feels like an invitation for a new research project of some kind.

Discovered a quirky museum spotlighting local artists and found in their gift shop a zine that a former student of mine contributed to. Double win!

Reddit is a really interesting example of digital labor issues as they relate to both social media and AI. I wonder how things will go over the next few years.

Bon, je comprends ces soucis, mais je ne suis pas sûr que de telles interdictions soient la bonne réponse. Pourtant, vu que je suis plus ouvert à une interdiction de l’IA à l’école, il faut que je développe un peu plus ma philosophie ici.

I’m being a little hard on this book because it wasn’t quite was I was expecting. There’s a lot of good stuff in here despite not having some of what I was hoping to find. I do wonder if it would have been better as the original lecture it’s based on: You could hear some of the audio, and I think some of the fat could be trimmed from the manuscript. I enjoyed reading it, I just wasn’t wowed by it.

I feel like we need to talk more about epistemology—and especially the social construction of facts—in the era of chatbot LLMs.

Bookmarked this a while ago and am finally reading it. So infuriating.

Dr. Calvin discovers the labor problems in academic publishing.

Lots of good observations in here, and I need to think through the implications for digital methods research.

I know what this school means, and I hate feeling like I’m playing ivory tower definitions games, but it REALLY bugs me when we limit “technology” to “things with chips in them.” A wiffle ball is technology, too.

Who schedules an academic conference to overlap with Halloween? And how long ago did they stop having kids at home?

What’s happening at the NSF is a tragedy, and I’m upset about all of these cuts. That said, I’ve long been skeptical about how the NSF has been used to promote STEM education at the expense of other worthy (but less economically productive) causes in schools. If Trump’s petty—and often cruel—cuts are a warning sign about how government can distort research priorities, there’s a deeper issue lurking in the background that we also need to wrestle with. [gift link]

I confess that I would have found this interesting in an earlier part of my career. Now, though, I’m reminded that I built that career on a methodological approach that’s uncomfortable close to surveillance, and I don’t love that.

I hate everything in this article.

There are some critical perspectives in this piece, but certainly not enough in my book. [gift link]

Trying to figure out whether hate listening to the audiobook of Moneyball has implications for my suitability to teach my department’s Intro to Data Science class.