Protect Me From What I Want

The Facebook whistleblower says its algorithms are dangerous. Here’s why.

Frances Haugen’s testimony at the Senate hearing today raised serious questions about how Facebook’s algorithms work—and echoes many findings from our previous investigation.

Private and Public Mastodon

Here’s how to get into a lot of trouble: Suppose you (like me) love the intellectual wealth found in free-form text on the Internet. And (like me) are a reasonably competent programmer. And (like me) have derived value and pleasure searching Twitter. And (like me) you look at this nifty new Fediverse thing and see that it has nice Web APIs so you could build an app to vacuum up all the stories and laments and cheers and dunks and love letters and index ’em and let everyone search ’em and find wonderful things! So you lurch into the Mastodon conversation, all excited, and blurt out “Hey folks, I’m gonna index all this stuff and let the world in!” That’s when you get your face torn off.

Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity

U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize “engagement”—serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds—for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that “surveillance capitalism” uses optimized algorithmic feedback for “automated behavioral modification” at scale, writing the “music” that users then “dance” to.

Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity

U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize “engagement”—serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds—for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that “surveillance capitalism” uses optimized algorithmic feedback for “automated behavioral modification” at scale, writing the “music” that users then “dance” to.

Understanding Decentralized Social Feed Curation on Mastodon

Amid rising concerns over moderation, algorithmic control, and platform governance on centralized social media, people are increasingly turning to decentralized alternatives like Mastodon to regain control over their feeds. This shift offers new opportunities to understand how people perceive and curate their feeds. We conducted a two-part study with 21 Mastodon users: first, interviews exploring how they perceive and manage their feeds; and second, a design probe study using BRAIDS.SOCIAL, a web-based feed curation prototype informed by the first part of our initial findings. We learned how seamful design can increase people's trust in algorithmic curation, and surfaced trade-offs people navigate between machine learning-based and rule-based filtering approaches. We also identify a core design tension in decentralized platforms: whether to support personalization through new applications or extensions layered atop existing ones.

Mastodon updates terms of service to ban AI model training on user data

Scraping user data to train AI models is a growing concern, and Mastodon has responded by banning the use of its content for such purposes.

Mastodon Features That Twitter Should Steal (but Won’t)

Elon Musk's platform could learn a thing or two from its most popular alternative—like how to build a social platform people actually want to be on.

Social Media Algorithms Distort Social Instincts and Fuel Misinformation - Neuroscience News

Social media algorithms, designed to boost user engagement for advertising revenue, amplify the biases inherent in human social learning processes, leading to misinformation and polarization.

Mastodon and federation

The more I read about Mastodon promoters' opinions on how it should work, the more I think, "How could you possibly think that is how it is going to go?" One group of people want a Federated Feed Reader: They want to read the clever updates and memes from their friends and favorite celebrities. They want to post a GIF and have their friends validate them by hitting "like" on it. They want to ...

Don't Frack. Precipitate!

Don’t Frack. Precipitate! Two opposing approaches to create soft software for the age of AI Author: If you stop someone on the street and ask them to imagine the canonical piece of software, what will they say? Almost certainly something like “Instagram.” An app. Apps have become such a dominan...

How Facebook got addicted to spreading misinformation

The company’s AI algorithms gave it an insatiable habit for lies and hate speech. Now the man who built them can't fix the problem.

Facebook Tried to Make Its Platform a Healthier Place. It Got Angrier Instead.

A big 2018 algorithm change was designed to encourage positive interaction with friends and family. Company documents show how it had the opposite effect, rewarding outrage and sensationalism. CEO Mark Zuckerberg resisted proposed fixes if it meant harming the business.

Algorithms may be making political polarization worse than we think

Marc Torrens and Carlos Carrasco-Farré analyze how algorithms influence opinions and heighten our emotional responses.

Deconstructing the algorithmic sublime

This special theme contextualizes, examines, and ultimately works to dispel the feelings of “sublime”—of awe and terror that overrides rational thought—that much of the contemporary public discourse on algorithms encourages. Employing critical, reflexive, and ethnographic techniques, these authors show that while algorithms can take on a multiplicity of different cultural meanings, they ultimately remain closely connected to the people who define and deploy them, and the institutions and power relations in which they are embedded. Building on a conversation we began at the Algorithms in Culture conference at U.C. Berkeley in December 2016, we collectively study algorithms as culture (Seaver, this special theme), fetish (Thomas et al.), imaginary (Christin), bureaucratic logic (Caplan and boyd), method of governance (Coletta and Kitchin; Lee; Geiger), mode of inquiry (Baumer), and mode of power (Kubler). [Box: see text]