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The article perceives AI systems with machine-learning components as having a recursive element when interacting with children. They can learn from an encounter with children and incorporate data from interaction, not only from prior programming. Given the purpose of growth and this recursive element of AI, the article argues for distinguishing the interpretation of\u00a0bias\u00a0within the artificial intelligence (AI) ethics and responsible AI discourse. Interpreting\u00a0bias\u00a0as a preference and distinguishing between positive (pro-diversity) and negative\u00a0(discriminative) bias is needed as this would serve children's healthy psychological and moral development. The human-centric AI discourse advocates for an alignment of capacities of humans and capabilities of machines by a focus both on the purpose of humans and on the purpose of machines for humans. The emphasis on mitigating negative biases through data protection, AI law, and certain value-sensitive design frameworks demonstrates that the purpose of the machine for humans is prioritized over the purpose of humans. These top\u2013down frameworks often narrow down the purpose of machines to do-no-harm and they miss accounting for the bottom-up views and developmental needs of children. Therefore, applying a growth model for children-AI interactions that incorporates learning from negative AI-mediated biases and amplifying positive ones would positively benefit children\u2019s development and children-centric AI innovation. Consequently, the article explores:<jats:italic>What challenges arise from mitigating negative biases and amplifying positive biases in children-AI interactions and how can a growth model address these?<\/jats:italic>To answer this, the article recommends applying a growth model in open AI co-creational spaces with and for children. In such spaces human\u2013machine and human\u2013human value alignment methods can be collectively applied in such a manner that children can (1) become sensitized toward the effects of AI-mediated negative biases on themselves and others; (2) enable children to appropriate and imbue top-down values of diversity, and non-discrimination with their meanings; (3) enforce children\u2019s right to identity and non-discrimination; (4) guide\u00a0children in developing an inclusive mindset; (5) inform top-down normative AI\u00a0frameworks by children\u2019s bottom-up views; (6) contribute to design criteria for children-centric AI. Applying such methods under a growth model in AI co-creational spaces with children could yield an inclusive co-evolution between responsible young humans in the loop and children-centric AI systems.<\/jats:p>","DOI":"10.1007\/s00146-022-01579-9","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T08:02:41Z","timestamp":1667030561000},"page":"1303-1315","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Toward children-centric AI: a case for a growth model in children-AI interactions"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2030-7555","authenticated-orcid":false,"given":"Karolina","family":"La Fors","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"1579_CR1","unstructured":"Aantal privacyklachten blijft zorgwekkend hoog\u201412\/03\/2021. 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