KidRec 2025

Over the past six editions, the Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems Workshop (KidRec) has been a space (first at ACM RecSys and then at ACM IDC) to bring together researchers, developers, practitioners and policymakers to share best practices and insights on creating trustworthy recommendation and search experiences for young users. The community we have built along the way has focused on exploring the design, development, and assessment of Information Retrieval Systems (IRS) that have children as the main stakeholders and actors. Together, we have also explored the ethics, policy, and other related aspects, seeking to ensure that they are designed to be child-centred, transparent, and responsible.

However, we have seen that children increasingly interact with artificial intelligence (AI)-driven search and recommendation systems, shaping their access to information, entertainment, and learning. More so, the go-to source for information access seems to be shifting to Social Networks (SN) which influence the type of resources children can seek for or are exposed to as a result of the recommender systems on these platforms. With that in mind, we propose the 7th edition of Kidrec to focus on the positive impact of SN and AI on IRS while keeping child involvement in the design process and ethical considerations in IRS for children at its core as in previous editions. By bridging research and practice, KidRec fosters a broader dialogue on the responsible development of AI-powered search and recommendation technologies that empower and protect young users.

Some relevant questions to the topic include:

• How can children be engaged not just as users but as co-designers of search and recommendation algorithms in this AI and SN world?
• How can AI tools support and scaffold children's learning without infringing upon or replacing their learning process?
• How can social networks serve as valuable information resources for children, rather than being excluded due to concerns about validity and trustworthiness?
• How can AI balance personalization with diversity, equity, and inclusion (DEI), transparency, and fairness in recommendations?
• How can search on social networks ensure safety and privacy while also upholding DEI, transparency, and fairness?