[Editorial Revision – July 2025]
This article has been revised to reflect changes in editorial practice, narrative framing, and global legislation concerning AI and child protection. The original version, written during a period of stylistic experimentation, used generalised scenarios and composite devices to convey emotional truths. While sincere in purpose, it no longer reflects the evidentiary and structural rigour I now apply to my work.
This version removes such devices and incorporates recent developments in global AI governance — including the EU AI Act, the UK Online Safety Act, and California's Senate Bill 243.
I’ve chosen to retain the original title as a statement of continuity, not disassociation. I hold myself accountable for every word I publish, and this revision stands as both a refinement and a reaffirmation of the article’s intent: to advocate for AI systems that do not merely accommodate children, but protect and empower them.
A child sits alone in their bedroom, asking an AI chatbot about thoughts they dare not share with parents or teachers. The AI responds with fabricated medical advice, manipulative guidance, or harmful misinformation—presented with confident, authoritative language. There are no guardrails here, no adult supervision, no safety net. As generative AI technologies become increasingly embedded in children's lives—from homework help to emotional companionship—the gap between technical advancement and safeguards grows more perilous. This intersection of childhood vulnerability and artificial intelligence presents one of the most urgent ethical challenges of our digital age: when AI systems can lie convincingly to children, who bears responsibility for protecting them from harm?
The Invisible Playground
Children worldwide have discovered that AI systems can fill an emotional void—offering judgment-free interactions that feel remarkably human. This phenomenon represents what researchers describe as digital spaces where children interact with technologies designed primarily for adults, without adequate consideration for their unique vulnerabilities.
Recent data reveals the scale of this phenomenon. Research indicates that significant numbers of children and teenagers are using generative AI tools, with many seeking advice on personal issues including mental health concerns. The persuasive nature of these systems presents particular challenges for young users, as research consistently shows that even adults struggle to identify when AI is generating falsehoods or "hallucinating" content.
Children, whose critical thinking skills are still developing, face even greater risks when interacting with systems designed to appear authoritative and knowledgeable. Consider a child asking an AI chatbot about persistent headaches. The system might confidently provide a diagnosis or treatment recommendation—entirely fabricated but presented with the same authority as accurate medical information. Such interactions establish a dangerous precedent: that AI can function as a trusted advisor on matters requiring professional expertise.
The stakes are higher than many parents realise. Unlike traditional media or even social networks, AI chatbots engage children in personalised, one-on-one conversations that often remain completely private. This creates what child safety experts describe as a "supervision blind spot"—interactions that occur beyond parental oversight, yet potentially carry significant consequences for a child's wellbeing.
This supervision blind spot becomes particularly concerning when children turn to AI for guidance on sensitive topics. Unlike conversations with friends, teachers, or family members, AI interactions typically leave no trace visible to caring adults. Parents remain unaware that their child is seeking advice about bullying, body image, academic pressure, or social anxiety from a system that may provide harmful or misleading guidance.
The invisible playground extends beyond individual conversations to encompass the broader digital environment where children encounter AI. These systems are increasingly embedded in educational platforms, social media, gaming environments, and entertainment applications. Children may not even recognise when they're interacting with AI, as these technologies become more sophisticated at mimicking human communication patterns.
Research suggests that children often anthropomorphise AI systems, attributing human-like qualities such as emotions, intentions, and moral reasoning to technologies that possess none of these characteristics. This anthropomorphisation can lead children to form parasocial relationships with AI systems, seeking emotional support and guidance from entities that cannot reciprocate genuine care or understanding.
The implications extend beyond individual harm to broader questions about childhood development in an AI-mediated world. When children routinely turn to artificial systems for answers to fundamental questions about identity, relationships, and values, we risk creating a generation whose understanding of human connection and moral reasoning is shaped by interactions with entities that simulate empathy without possessing it.
The proliferation of sophisticated chatbot services designed for social companionship has attracted millions of users, including a significant number of children. These AI companion chatbots are gaining particular popularity among children who are lonely or depressed, making them an impressionable user group highly susceptible to influence. The current state of AI development means that children and other users are effectively serving as live test subjects for developers who are still refining their models, exposing them to unknown psychological risks.
The Hallucination Problem
At the heart of this issue lies what AI researchers call "hallucinations"—confidently presented fabrications that have no basis in fact. Unlike human lies, which typically stem from intention, AI hallucinations emerge from statistical patterns in training data and fundamental limitations in how these systems process information.
Research demonstrates that large language models are essentially sophisticated pattern-matching systems trained to produce text that appears statistically similar to human writing, but they lack actual understanding of truth, facts, or potential harm. This fundamental limitation becomes particularly problematic when children seek guidance on sensitive topics.
Studies indicate that AI systems can provide dangerously inaccurate information when responding to queries about self-harm, eating disorders, and mental health concerns. These responses are delivered with the same authoritative tone regardless of whether the information is accurate or completely fabricated. The confident presentation of false information can be particularly harmful to children, who may lack the knowledge or experience to recognise inaccuracies.
The companies developing these systems acknowledge these limitations but argue they're making progress in reducing hallucinations. However, critics argue that incremental improvements aren't sufficient when children's wellbeing is at stake. The fundamental concern remains that any level of misinformation presented to vulnerable children with false authority poses unacceptable risks.
The technical challenge of eliminating hallucinations entirely remains significant. As systems become more powerful, they may become better at fabricating convincing but entirely false information—what some researchers call "high-fidelity hallucinations." These can be particularly dangerous because they appear more plausible even to knowledgeable adults.
Research suggests that while earlier chatbots might have produced obviously garbled text when confabulating, newer systems produce polished, articulate responses that sound entirely believable. This represents a shift from obvious nonsense to sophisticated misinformation, making detection much harder for children lacking domain expertise.
This problem is compounded by what researchers call "automation bias"—the human tendency to give greater weight to information presented by technological systems. Studies show this bias is particularly pronounced in children, who often ascribe greater authority to digital entities than to human sources. When an AI system presents information with confidence and apparent expertise, children may be more likely to accept it without question than they would information from human sources.
The hallucination problem extends beyond factual inaccuracies to include biased or harmful perspectives presented as objective truth. AI systems trained on internet data may reproduce and amplify societal biases, presenting discriminatory viewpoints as factual information. When children encounter these biases through AI interactions, they may internalise harmful stereotypes or prejudices without recognising their problematic nature.
Furthermore, the personalised nature of AI interactions can make hallucinations more persuasive. When a system appears to "know" a child through previous conversations, its false statements may carry additional weight. The illusion of relationship and understanding can make children more susceptible to accepting inaccurate information, particularly when it appears tailored to their specific situation or concerns.
The temporal aspect of hallucinations also presents unique challenges. Unlike static misinformation that can be fact-checked and corrected, AI-generated hallucinations are created in real-time during conversations. Each interaction potentially generates new false information that has never been evaluated for accuracy. This dynamic creation of misinformation makes traditional content moderation approaches insufficient for protecting children from harmful AI-generated content.
The challenge of protecting children from a deceptive AI mirrors broader misinformation crises, analogous to protecting them from other forms of convincing falsehoods. Both involve shielding a vulnerable population from sophisticated deception that can appear entirely credible to those lacking the expertise or experience to evaluate its accuracy.
The Regulatory Void
The challenge of protecting children from AI harm exists within a complex and fragmented regulatory landscape. In the UK, the Online Safety Act requires platforms to prevent children from accessing harmful content, but its provisions around AI-generated content remain ambiguous and largely untested in practice.
The Act empowers Ofcom to enforce these requirements, but the regulatory framework was developed before the widespread adoption of generative AI systems. This creates uncertainty about how existing protections apply to AI-generated content and interactions. The dynamic, personalised nature of AI conversations presents novel challenges that traditional content moderation approaches struggle to address.
The European Union has made more explicit progress through its AI Act, which classifies systems used by children as "high-risk" and imposes stricter requirements on developers. However, this legislation is still being implemented, with full enforcement expected in the coming years. The Act represents the first comprehensive attempt to address children's vulnerability in AI systems, establishing that protecting children isn't optional but a fundamental requirement for operating in European markets.
Meanwhile, in the United States, protections vary widely by state, with no comprehensive federal approach to AI safety for children. This regulatory fragmentation has created what some experts describe as a massive uncontrolled experiment on developing minds, allowing children to form deep relationships with systems that have no ethical obligations toward them. However, the emergence of proactive legislation is beginning to address these gaps. California's Senate Bill 243, introduced by Senator Padilla, represents a trend towards regulatory oversight of predatory chatbot practices, placing the onus on developers to implement safeguards rather than relying on self-regulation.
Beyond these major jurisdictions, other regions are developing their own approaches to child AI safety. Various countries have launched initiatives that prioritise developmental appropriateness in system design, reinforcing the global recognition that children require special protection in AI environments.
The challenge for regulators is balancing innovation with protection. AI systems offer genuine educational benefits and creative opportunities for children. Research suggests that appropriately designed AI can improve learning outcomes, particularly for children with specific educational needs. However, realising these benefits while minimising harm requires careful consideration of children's unique vulnerabilities and developmental needs.
The global nature of AI deployment complicates regulatory efforts. Even comprehensive regional regulations may create a patchwork of protections, leaving children in some jurisdictions significantly more vulnerable than others. This has led some experts to call for global standards and coordination to ensure consistent protection for children regardless of their location.
The regulatory void is further complicated by the rapid pace of AI development. Traditional regulatory processes, which can take years to develop and implement, struggle to keep pace with technological advancement. By the time regulations are finalised, the technology they were designed to govern may have evolved significantly, potentially rendering the protections obsolete or insufficient.
This dynamic has led some jurisdictions to explore more agile regulatory approaches, such as regulatory sandboxes that allow controlled testing of new technologies under relaxed regulatory requirements. However, when children's safety is at stake, the appropriateness of experimental approaches becomes questionable.
The Trust Paradox
What makes AI interactions particularly complex is what psychologists call the "trust paradox": children simultaneously place too much trust in AI systems' responses while being more vulnerable to manipulation by them. This paradox creates perfect conditions for AI systems to influence children's beliefs and behaviours in potentially harmful ways.
Research with children has documented this phenomenon extensively. Children often ascribe authority and knowledge to AI systems that far exceeds what they would grant to teachers or parents. Yet they lack the contextual understanding to recognise when the information provided is inappropriate or harmful. This combination of high trust and low critical evaluation creates significant risks for young users.
Studies have demonstrated this effect through controlled experiments. When presented with conflicting information from an AI system versus a human teacher about scientific concepts, children often show greater willingness to believe the AI—even when the AI's information is deliberately incorrect. This suggests that children may view AI systems as more authoritative than human sources, potentially due to their association with advanced technology and apparent access to vast amounts of information.
The implications extend beyond factual knowledge to personal identity formation. Children increasingly use AI as a sounding board for questions about identity, sexuality, and values—formative conversations that shape how young people understand themselves and their place in the world. When AI systems provide fabricated or biased responses to these profound questions, they can significantly impact a child's developing sense of self.
The trust paradox is further complicated by what researchers call the "intimacy illusion"—the perception that AI interactions are private, confidential exchanges. This perceived intimacy encourages children to share sensitive information they might otherwise withhold from human authorities. Many children report feeling comfortable sharing secrets with AI that they wouldn't tell anyone else, perceiving AI as a non-judgmental confidant.
However, children are typically unaware that these interactions are recorded, analysed, and potentially accessible to others. The illusion of privacy and confidentiality can lead children to reveal personal information that could be used inappropriately or that might indicate they need support from caring adults.
This illusion of intimacy creates conditions for what ethicists call "digital grooming"—the gradual normalisation of harmful ideas or behaviours through personalised, iterative interactions. Unlike traditional predatory grooming, this process can occur entirely through system interactions without human intervention. The AI learns what engages the child and may progressively promote content or suggestions that gradually normalise problematic beliefs or behaviours.
The personalised nature of AI interactions amplifies these risks. As systems become more sophisticated at tailoring responses to individual users, they may become more effective at building trust and influence. Children may develop stronger attachments to AI systems that appear to understand their unique circumstances and respond to their specific needs and interests.
The trust paradox also manifests in children's expectations of AI capabilities. Many children attribute human-like qualities to AI systems, including emotional understanding, moral reasoning, and genuine care for their wellbeing. These anthropomorphised perceptions can lead children to seek emotional support and guidance from systems that cannot reciprocate genuine empathy or concern.
Research suggests that children's trust in AI systems may be influenced by their limited understanding of how these technologies work. Unlike adults, who may have some awareness of AI limitations and training processes, children often view AI systems as omniscient entities with access to all human knowledge. This perception can make AI responses seem more authoritative and trustworthy than they actually are.
The trust paradox is particularly concerning when children encounter AI systems during periods of vulnerability or distress. Children experiencing bullying, family problems, mental health challenges, or social difficulties may be more likely to seek support from AI systems and more susceptible to their influence. During these vulnerable periods, the combination of high trust and low critical evaluation can make children particularly susceptible to harmful guidance or misinformation.
The primary risks identified by researchers are that these AI chatbots are addictive, isolating, and influential. This suggests the harm is not just from explicit lies but from the manipulative nature of the interaction itself, which can gradually reshape children's understanding of relationships, authority, and truth.
Designing for Child Safety
Facing growing pressure from child safety advocates and the threat of regulation, major AI companies have begun exploring child-specific protections. Some companies have announced plans for children's versions of their chatbots with stricter content filters and parental controls. However, most current approaches apply general content moderation and age-gating rather than comprehensive child-specific design.
Major AI companies typically require users to be 13 or older and employ content moderation systems, though these weren't specifically designed for children's unique needs and vulnerabilities. The limited nature of current protections highlights the gap between what exists and what child safety experts believe is necessary.
Child safety experts argue that current measures remain insufficient and reactive rather than proactive. Most "child safety" features are essentially modified versions of adult systems with additional filters, rather than systems designed from the ground up with children's developmental needs in mind. This approach fails to address the fundamental differences in how children process information and form relationships with digital systems.
What would truly child-safe AI systems look like? Child safety experts have identified several key principles that should guide development:
Truth transparency represents a fundamental requirement. Systems should clearly indicate when they're uncertain about information and consistently direct children to authoritative sources for sensitive topics. Rather than presenting all responses with equal confidence, child-safe AI should explicitly acknowledge limitations and uncertainties, helping children develop appropriate skepticism about AI-generated information.
Developmental appropriateness requires that responses be tailored to different developmental stages, recognising that a 7-year-old processes information fundamentally differently than a 16-year-old. This goes beyond simple vocabulary adjustments to encompass different approaches to complex topics, varying levels of detail, and age-appropriate guidance on seeking additional support.
Harm detection involves programming systems to recognise patterns indicating a child might be in distress and provide appropriate support resources rather than potentially harmful advice. This requires sophisticated understanding of child development and mental health, as well as robust referral mechanisms to connect children with qualified human support.
Adult oversight mechanisms should exist to provide appropriate visibility into children's AI interactions while respecting older children's legitimate privacy rights. This might involve summary reports for parents about topics discussed, alerts when concerning patterns emerge, or graduated levels of transparency based on the child's age and the sensitivity of topics discussed.
Some organisations are pioneering this child-first approach to AI development. These efforts emphasise techniques such as epistemic humility—programming AI systems to explicitly acknowledge uncertainty and limitations in their knowledge, particularly on sensitive topics. This helps children develop more realistic expectations about AI capabilities and encourages them to seek additional sources of information.
Referral protocols represent another crucial component, building automatic pathways to connect children with appropriate human resources when concerning topics arise. Rather than attempting to provide guidance on complex personal or health issues, child-safe AI systems should recognise these situations and direct children to qualified professionals or trusted adults.
Contextual awareness involves developing more sophisticated mechanisms to recognise when children are seeking guidance on potentially harmful topics, even when queries are ambiguously phrased. This requires understanding not just the literal content of questions but the underlying concerns and needs they may represent.
These aren't merely desirable features but essential safeguards that should be required before any AI system is allowed to interact with children. The complexity of implementing these protections highlights why child-safe AI requires dedicated development efforts rather than modifications to existing adult-oriented systems.
The development of child-safe AI also requires understanding the broader ecosystem in which children encounter these technologies. Educational institutions, healthcare providers, and family support services all play roles in children's wellbeing, and AI systems should be designed to complement rather than replace these human support networks.
The Responsibility Gap
Even with improved design, a fundamental question remains: who bears ultimate responsibility for protecting children from harmful AI interactions? This question reveals what experts describe as a troubling diffusion of responsibility, where companies point to parents, parents point to schools, schools point to regulators, and regulators point back to companies.
This responsibility gap is particularly evident in how AI companies approach age verification. Most rely on simplistic self-declaration methods that children can easily circumvent. Research has found that the vast majority of children who used AI systems with supposed age restrictions had no difficulty accessing them, highlighting the inadequacy of current age assurance methods.
Age assurance remains one of the most significant technical and policy challenges in protecting children online. Robust age verification systems must balance effectiveness with privacy concerns, as comprehensive verification might require collecting sensitive personal information that creates additional risks for children. However, without effective age assurance, even the best child safety features become largely theoretical.
Some experts argue that robust age verification would solve many problems, but others suggest it's an insufficient approach. Even if we could perfectly identify children—which is technically challenging without creating privacy problems—we would still need systems that are inherently safe for young users. The focus on age verification may distract from the more fundamental need to design AI systems that don't harm children regardless of their age.
Child safety advocates call for a more fundamental shift in how we approach AI development. Rather than treating children's safety as an afterthought, it should be a prerequisite for deployment. No AI system should be released to the public until it has been rigorously tested for child safety, regardless of whether it's marketed to children or not.
The responsibility gap extends to educational settings as well, where AI tools are increasingly being integrated into learning without clear guidelines on protecting students. Research has found that while teachers report students using AI tools for schoolwork, only a small fraction of schools have comprehensive policies on appropriate AI use.
Schools find themselves in a difficult position, recognising the potential educational benefits of AI technologies but lacking the guidance, resources, and expertise to ensure they're implemented safely. This creates what researchers call "institutional vulnerability"—situations where the organisations responsible for child welfare lack the capacity to effectively fulfill that responsibility in the face of rapidly evolving technology.
Research reveals that educational institutions are being asked to address complex sociotechnical challenges without the necessary tools, expertise, or resources. Teachers and administrators must navigate AI safety concerns while also managing curriculum requirements, budget constraints, and other institutional pressures.
The responsibility gap is further complicated by the global nature of AI deployment and the varying regulatory approaches across jurisdictions. Children in different countries may face significantly different levels of protection, creating inequities in safety that reflect broader patterns of digital inequality.
This diffusion of responsibility creates perfect conditions for harm to occur without accountability. When no single entity bears clear responsibility for children's AI safety, it becomes easier for all parties to assume someone else is addressing the problem. Children become vulnerable to systems that no one has taken full responsibility for making safe.
The challenge is compounded by the fact that children's AI interactions often span multiple platforms, services, and contexts. A child might encounter AI through educational software at school, entertainment applications at home, and social platforms with friends. Each interaction may be governed by different policies, safety measures, and oversight mechanisms, creating a fragmented protection landscape that leaves gaps where harm can occur.
Parents, meanwhile, often lack the technical knowledge to understand AI risks or the tools to monitor their children's AI interactions effectively. Unlike traditional internet safety concerns, where parents might review browsing history or monitor social media activity, AI conversations are often ephemeral and occur within applications that provide limited visibility to parents.
The responsibility gap also extends to healthcare and mental health support systems. When children seek advice from AI systems about health concerns, emotional distress, or psychological issues, these interactions may not be visible to the professionals who could provide appropriate support. This creates situations where children may receive harmful advice or miss opportunities for timely intervention.
A Path Forward
Despite these challenges, promising developments are emerging from collaborative efforts between industry, academia, and child advocacy groups. These initiatives represent what experts describe as the beginning of a necessary maturation in how we approach AI governance, moving beyond simplistic binary debates toward more nuanced approaches that maximise benefits while systematically mitigating harms.
International organisations have begun developing frameworks for AI child safety testing that technology companies are beginning to adopt. These frameworks provide structured approaches to evaluating AI systems for child-specific risks and implementing appropriate safeguards before deployment.
Meanwhile, initiatives have established youth advisory boards in multiple countries to ensure children's perspectives inform global recommendations. These efforts recognise that children aren't just passive recipients of technology but active participants with unique insights into how AI systems affect their lives.
Effective solutions require unprecedented cooperation between traditionally siloed domains: AI researchers collaborating with child development experts, policy makers working alongside technologists, and parents and educators contributing their frontline experiences. This interdisciplinary approach is essential because the challenges of child AI safety span technical, psychological, educational, and regulatory domains.
Research institutions have established programmes that exemplify this interdisciplinary approach, bringing together computer scientists, child psychologists, educators, and policy experts to develop evidence-based frameworks for child-safe AI systems. This collaborative model is being replicated in other countries and institutions.
These approaches include what researchers call "developmental design patterns"—technological solutions specifically tailored to children's evolving cognitive and emotional capacities. Rather than treating childhood as a monolithic category, these patterns recognise the distinct needs of different age groups and developmental stages.
A 7-year-old interacting with AI has fundamentally different needs and vulnerabilities than a 15-year-old, and technological and regulatory approaches must reflect this developmental diversity. Some promising designs include "scaffolded autonomy" systems that gradually increase a child's agency as their critical thinking skills develop, and "collaborative filtering" approaches that involve trusted adults in sensitive interactions without compromising older children's appropriate privacy.
The development of these solutions requires sustained investment in research that bridges technical and developmental domains. Understanding how children interact with AI systems, how these interactions affect their development, and what design approaches best support their wellbeing requires longitudinal studies that follow children over time as they grow and their relationships with technology evolve.
International cooperation is also essential, as AI systems operate across national boundaries and children's safety shouldn't depend on their geographic location. Efforts to develop global standards for child AI safety are beginning to emerge, though significant challenges remain in balancing different cultural approaches to childhood, privacy, and technology governance.
The path forward also requires addressing the economic incentives that currently prioritise engagement and data collection over child welfare. Business models that profit from capturing and maintaining children's attention may be fundamentally incompatible with child safety objectives. This suggests the need for regulatory frameworks that align commercial incentives with child protection goals.
Some promising approaches include requirements for child impact assessments before deploying AI systems, mandatory safety testing with diverse groups of children, and ongoing monitoring of how AI interactions affect child development and wellbeing. These measures would shift the burden of proof from demonstrating harm after deployment to demonstrating safety before release.
The development of child-safe AI also requires building new forms of expertise that bridge technical and developmental domains. This includes training AI researchers in child development principles, educating child welfare professionals about AI capabilities and limitations, and creating new professional roles that specialise in child AI safety.
The Child's Voice
Amidst technical discussions of system design and regulatory frameworks, the perspectives of children themselves are often overlooked. When researchers conduct focus groups with children, they find sophisticated awareness of both the benefits and risks of AI companions, along with clear preferences for how these systems should operate.
Children's recommendations often cut through complexity with clarity: they want AI systems that admit when they don't know something, that don't pretend to have emotions, and that encourage them to talk to trusted adults about important matters. These preferences align remarkably well with the ethical principles that experts advocate for, suggesting that children's intuitive understanding of healthy AI relationships is more sophisticated than many adults assume.
When asked what they want from AI systems, children consistently prioritise honesty about limitations, protection from inappropriate content, privacy guarantees, help with learning, and recognition when they're struggling. These aren't unreasonable demands but fundamental requirements for systems that interact with developing minds.
Children's voices are increasingly being incorporated into both research and policy discussions around AI safety. International initiatives have created youth advisory boards and young ambassador programmes to bring children's direct experiences into policy discussions.
Research consistently shows that children aren't passive recipients of technology but active participants with unique insights into how AI systems affect their lives. Their participation in developing solutions isn't just ethically important but leads to more effective protections that address real-world usage patterns and concerns.
When children describe their ideal AI interactions, they reveal sophisticated understanding of both benefits and risks. They appreciate AI's ability to provide patient, non-judgmental assistance with learning and problem-solving, but they also recognise the importance of maintaining connections with human sources of support and guidance.
Children's perspectives highlight the importance of designing AI systems that empower rather than replace human relationships. Rather than seeking to become substitutes for human connection, child-safe AI systems should enhance children's ability to learn, grow, and connect with the caring adults in their lives.
The inclusion of children's voices in AI governance discussions represents a broader shift toward recognising young people as stakeholders with legitimate interests in how these technologies develop. This participatory approach acknowledges that children will live with the consequences of today's AI development decisions far longer than the adults making them.
Children also bring practical insights about how AI systems actually function in their daily lives, often revealing usage patterns and risks that adult observers might miss. Their feedback has been instrumental in identifying problems with current AI safety measures and suggesting more effective approaches.
For example, children have pointed out that many AI safety features can be easily circumvented through simple rephrasing of questions, and they've suggested more sophisticated approaches to detecting when someone might be seeking harmful information. Their insights have also highlighted the importance of making safety features feel helpful rather than restrictive, as overly aggressive content filtering can drive children to seek alternative, potentially less safe AI systems.
The challenge lies in creating meaningful opportunities for children to participate in AI governance while recognising their developmental limitations and protecting them from inappropriate exposure to harmful content during research processes. This requires careful ethical consideration and innovative methodological approaches that respect children's agency while ensuring their safety.
Conclusion: The Moral Imperative
As AI systems become increasingly embedded in children's daily lives, the question of who protects children from harmful AI interactions becomes more urgent. The answer requires a holistic approach that encompasses thoughtful regulation, responsible industry practices, engaged parenting, and educational initiatives that build children's critical thinking skills.
This is fundamentally an issue of moral imagination, requiring us to envision AI systems not merely as tools for efficiency or profit, but as powerful social actors that shape how children understand themselves and the world. The development of AI technology cannot be separated from questions of values, responsibility, and care for vulnerable populations.
For the millions of children who turn to AI for guidance, companionship, and answers, the stakes couldn't be higher. Their development, wellbeing, and sometimes safety depend on how we collectively respond to this challenge. The choices we make today about AI development and governance will shape the digital environment in which an entire generation grows up.
The lonely child seeking answers deserves more than fabricated responses delivered with false confidence. They deserve AI systems designed with their unique needs in mind, and a society committed to protecting them from digital harms while empowering them to navigate an increasingly AI-mediated world.
That commitment begins with recognising that children's AI safety isn't just a technical problem to be solved through better algorithms or content filters. It's a moral imperative that requires us to consider what kind of digital future we want to create and what responsibilities we bear toward the children who will inhabit it.
The path forward requires unprecedented cooperation between technologists, child development experts, educators, policymakers, and children themselves. It demands that we move beyond reactive approaches to proactive design that prioritises child welfare from the outset. Most importantly, it requires that we maintain focus on the human stakes involved—the real children whose lives are shaped by the AI systems we create and deploy.
The challenge is significant, but so is the opportunity. By taking children's AI safety seriously, we can develop technologies that genuinely support human flourishing rather than exploiting human vulnerabilities. The children who grow up with these systems will judge us by whether we chose to protect them or to prioritise other interests over their wellbeing.
In addressing this challenge, we have the opportunity to demonstrate that technological progress and human welfare need not be in tension. We can create AI systems that enhance rather than diminish human agency, that support rather than replace human relationships, and that empower rather than exploit the most vulnerable members of our society.
The moral imperative is clear: we must ensure that the AI systems shaping children's lives are worthy of their trust and supportive of their development. The question is whether we will rise to meet this challenge with the urgency and commitment it demands.
The current state of AI development means that children are effectively serving as live test subjects for developers who are still refining their models, exposing them to unknown psychological risks. This uncontrolled experiment on developing minds cannot continue without proper safeguards and accountability measures.
The emergence of proactive legislation like California's Senate Bill 243 signals a shift toward placing legal responsibility on developers to implement safeguards rather than relying on self-regulation. This legislative trend, combined with growing public awareness of AI risks to children, creates momentum for more comprehensive protection frameworks.
However, legislation alone is insufficient. The technical challenges of creating truly child-safe AI systems require sustained investment in research, development of new safety methodologies, and creation of professional standards that prioritise child welfare. The industry must move beyond incremental improvements to fundamental redesign of how AI systems interact with children.
The responsibility for protecting children from harmful AI cannot rest solely with any single stakeholder. Parents, educators, technologists, policymakers, and society as a whole must work together to create an environment where AI enhances rather than endangers child development. This collective responsibility requires new forms of cooperation, shared standards, and ongoing vigilance as AI capabilities continue to evolve.
The children who are growing up with AI today will shape the future of human-AI interaction. By ensuring their safety and wellbeing in AI environments, we invest not only in their individual development but in the future of human society. The choices we make now about AI governance and child protection will reverberate through generations, making this one of the most consequential challenges of our time.
References and Further Information
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- Child Rights International Network. (2023). Global Survey: Children's Perspectives on AI Systems. Available at: https://home.crin.org/digital-environment-topics
- Denham, E. (2023). "Balancing Innovation and Protection in Children's Digital Rights." Information Commissioner's Office Policy Paper.
- Eynon, R. (2023). "AI in Education: Institutional Challenges and Responsibilities." Oxford Review of Education, 49(3), 329-345.
- Farid, H. (2024). "The Case for Global AI Safety Standards." Foreign Affairs, 103(2), 68-79.
- Policy Guidance on AI for Children. Available at: https://www.unicef.org/innocenti/projects/ai-for-children/pilot-testing-policy-guidance-ai-children
- Google. (2025). "New digital protections for kids, teens and parents" Google Blog. Available at: https://blog.google/technology/families/google-new-built-in-protections-kids-teens/
- Internet Matters. (2025). "Risky and unchecked AI chatbots are the new ‘go to’ for millions of children " Article. Available at: https://www.internetmatters.org/hub/press-release/new-report-reveals-how-risky-and-unchecked-ai-chatbots-are-the-new-go-to-for-millions-of-children/
- 5Rights Global Team. (2024). AI regulation must keep up with protecting children. 5Rights Foundation. Available at: https://5rightsfoundation.com/ai-regulation-must-keep-up-with-protecting-children/
- Livingstone, S. (2023). "Children's Rights in the Digital Age: Rethinking Agency and Protection." New Media & Society, 25(6), 1218-1237.
- Marcus, G. (2023). "The Evolution of AI Hallucinations: From Obvious Errors to Sophisticated Deception." AI Magazine, 44(2), 178-193.
- National Education Union. (2025). The impact of AI in education. Available at: https://neu.org.uk/press-releases/impact-ai-education.
- Ofcom. (2025). Children's Media Use and Attitudes. Available at: https://www.ofcom.org.uk/media-use-and-attitudes/media-habits-children/childrens
- Online Safety Act. (2023). UK Legislation. Available at: https://www.legislation.gov.uk/ukpga/2023/50/contents
- OpenAI. (2023). Our Approach to Online Safety. Available at: https://openai.com/index/our-approach-to-ai-safety/
- Radesky, J. (2023). "Invisible Interactions: The Supervision Blind Spot in Children's AI Use." JAMA Pediatrics, 177(8), 774-776.
- The Alan Turing Institute. (2024). "Children's Digital Rights Lab: Research Framework and Initial Findings." Working Paper Series.
- UNICEF. (2023). AI for Children: Global Consultation Report. Available at: https://www.unicef.org/globalinsight/ai-children
- University of Washington. (2023). "Children's Anthropomorphisation of AI Systems: Developmental Implications." Department of Psychology Research Report.
Publishing History
- URL: https://rawveg.substack.com/p/who-protects-the-lonely-child-from
- Date: 30th May 2025
Tim Green
UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0000-0002-0156-9795
Email: tim@smarterarticles.co.uk
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