How Platform Recommenders Work – Center for Human-Compatible Artificial Intelligence

A recommender system (or simply ‘recommender’) is an algorithm that takes a large set of items and determines which of those to display to a user—think the Facebook News Feed, the Twitter timeline, Google News, or the YouTube homepage. Recommenders are necessary tools to help navigate the sheer volume of content produced each day, but their scale and rapid development can cause unintended consequences. Facebook’s algorithms have been blamed for radicalizing users, TikTok’s for inundating teens with eating-disorder videos, and Twitter’s for political bias. 

The Algorithm, Singular

A new European ruling on algorithmic amplification determines if an operator controls how content spreads on a platform. But applying it to open social networks turns out to be a problem.

Eurosky is collaborating with SPRIND to test a non-toxic algorithm for the first time - Eurosky

Eurosky is a European initiative to build and operate sovereign social web infrastructure

Bridging-Based Ranking

There is significant concern about the engagement-based ranking systems used by TikTok, Facebook, YouTube, etc. to recommend content. Bridging-based ranking systems can address one of the most dangerous aspects of such algorithmic recommendations—the push toward polarization and divisiveness that is tearing nations apart—and do so without reducing anonymity or increasing censorship. This report explores what bridging-based ranking is, how it helps (overcoming downsides of chronological feeds and middleware), addresses common objections, and provides early examples of its use and benefits in the wild. The report concludes by providing next steps for platforms, governments, funders, and researchers in order to accelerate the deployment of bridging.

“Age limits on social media are a dead end”

Public authorities should focus on regulating algorithms and imposing stricter controls on data collection instead, argues researcher.

Understanding Social Media Recommendation Algorithms

Access the PDF version of this essay by clicking the icon to the right.

Social media algorithms can be redesigned to bridge divides — here’s how

"It falls to both the tech companies that built these systems and an engaged public to create technologies designed for social cohesion."

Privacy as EU Tech advantage - The LeafPlaza Blog

Online crime loves legal massive data collection, grey systems, and jurisdictional gaps. Privacy-first design can actually reduce crime exposure and build trust in digital services. It is also central to the EU's tech autonomy: build systems aligning with European rights and risk models. Do not rely on foreign platforms or copy outside practices that might go against the EU values and needs.

Letting Users Choose Recommender Algorithms: An Experimental Study

Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.

In Russmedia Ruling, the GDPR Displaces Europe's Rules for Online Speech

The ruling shows the serious problems that can arise when European courts rely solely on the GDPR, writes Daphne Keller.

Algorithm appreciation: People prefer algorithmic to human judgment

Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation, when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic attraction (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s; Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates.