This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.
A Literature Review of Ethical Considerations in Recommender Systems for User-Generated Content in Human-Computer Interaction
The design of user-generated content (UGC) platforms poses challenges in comprehensively addressing the ethical dimensions of recommendation algorithms and applying human-centered methods for their evaluation. This article presents a literature review of 97 studies on UGC algorithms (UGCAlgos) that incorporate human factors and user experience considerations to investigate the ethical issues explored in human-computer interaction (HCI) research. Our review identifies key themes in the ethical considerations surrounding UGCAlgos and the user modeling methods employed. We examine how common ethical concerns in recommender systems, such as content appropriateness, privacy, user engagement, transparency, fairness, and diversity, are studied and contextualized within UGC platforms. Furthermore, we summarize how these concerns are addressed through user modeling approaches, including data characterization, user context, user outsmarting, interface design, user beliefs, and community and societal impacts.
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.
Recommendation Systems - Not so discussed areas
Practical advice on recommendation systems in production
Mel Andrews on Twitter / X
Whether predictive systems can be ethically—or even functionally—used in socially sensitive contexts depends on their epistemic credentials. I am delighted to see my work inform policy recommendations in a new report from Amnesty International. https://t.co/NbQBM31N5M pic.twitter.com/EXPgxXgszF— Mel Andrews (@bayesianboy) June 11, 2026
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.
“Data Strikes”: Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies | The World Wide Web Conference
Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's ...
Ethical Source: Open Source, Evolved.
The Organization for Ethical Source is a global, multidisciplinary community devoted to centering justice, equity, and human rights in the practice of open source.
Ethical Source: Open Source, Evolved.
The Organization for Ethical Source is a global, multidisciplinary community devoted to centering justice, equity, and human rights in the practice of open source.
The Reversal Test: Eliminating Status Quo Bias in Applied Ethics
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Ads and Ad blockers
Last time, we shared how ethical advertising works to keep Read the Docs sustainable without creepy ad targeting. This time, we will share about one of our biggest challenges with advertising. At the beginning of April, Read the Docs was added to one of the most popular ad block lists: the Easyli...
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.
I read the new EU Court ruling on algorithms and social platforms so you don't have to. Turns out to be the most consequential thing a European court has said about recommendation algorithms, and also it just breaks when you apply it to the atmosphere connectedplaces.online/the-algorithm-singular/
The Algorithm, Singular
connectedplaces.onlineRecommender Systems
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BridgEU: new project to study how recommendation systems can reduce polarization | SynoSys
Letting Users Choose Recommender Algorithms: An Experimental Study
Agentic Taste Modeling | lab notes #8
A Bluesky feed for one · Adam Wiggins
How the Substack feed is learning to understand your reading journey
