Recommender systems and their ethical challenges

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.

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.

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.

This Mess We’re In

ethics, open source, and free riding

What We Believe

The Organization for Ethical Source is a global, multidisciplinary community devoted to centering justice, equity, and human rights in the practice of open source.

Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI

Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.

We oppose DRM. | Defective by Design

The Ethical Tech Giving Guide replaces DRM-laden software and devices that trample user freedom and privacy with products and programs that you can trust.

Research ethics: 3 ways to blow the whistle

Reporting suspicions of scientific fraud is rarely easy, but some paths are more effective than others.

Ethical Web Principles

The web should be a platform that helps people and provides a positive social benefit. As we continue to evolve the web platform, we must therefore consider the consequences of our work. The following document sets out ethical principles that will drive W3C's continuing work in this direction.