The algorithmic recommender systems that select, filter, and personalize experiences across online platforms and services play a significant role in shaping user experiences online. These systems largely determine what users see, read, and watch, fueling debates around their potential to amplify harmful content, foster societal division, and prioritize engagement over user well-being. In reaction, some policymakers have turned to blanket bans on personalization or to the promotion of chronological feeds. But there are many better alternatives. Suggesting that users must choose between today’s default feeds and chronological or non-personalized feeds creates a false choice.
This report, prepared by the KGI Expert Working Group on Recommender Systems, offers comprehensive insights and policy guidance aimed at optimizing recommender systems for long-term user value and high-quality experiences. Drawing on a multidisciplinary research base and industry expertise, the report highlights key challenges in the current design and regulation of recommender systems and proposes actionable solutions for policymakers and product designers.
A key concern is that some platforms optimize their recommender systems to maximize certain forms of predicted engagement, which can prioritize clicks and likes over stronger signals of long-term user value. Maximizing the chances that users will click, like, share, and view content this week, this month, and this quarter aligns well with the business interests of tech platforms monetized through advertising. Product teams are rewarded for showing short-term gains in platform usage, and financial markets and investors reward companies that can deliver large audiences to advertisers.
Concerns have been raised about the relationship between this design approach and a range of individual and societal harms, including the spread of low-quality or harmful content, reduced user satisfaction, problematic overuse, and increased polarization. Available evidence underscores the need for a shift towards designs that optimize for long-term user satisfaction, well-being, and societal benefits.