Parenting Journal · Volume 1 · Issue 2 · Parenting and AI

Respect

Children’s dignity, privacy, consent, cultural context and fairness in AI systems.

20 articles · Page 1 of 1

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21

Vol. 1 · Issue 2 · Respect · Qualitative youth interview study

Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care

How do young people want to participate when AI uses their health information?

Children and youths aged 10–17 yearsCC BY (version not stated)Mixed healthcare AI scenarios / youth values studyChild-facing evidence
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22

Vol. 1 · Issue 2 · Respect · Qualitative child and adolescent focus-group study

Children and adolescents‘ views on artificial intelligence in pediatric healthcare: a qualitative focus group study

What do children want adults to respect when AI is used in their healthcare?

Children and adolescents aged 8–17 yearsCC BY 4.0Mixed pediatric AI scenarios / youth perspectivesChild-facing evidence
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24

Vol. 1 · Issue 2 · Respect · Normative ethical analysis

AI-assisted consent in paediatric medicine: ethical implications of using large language models to support decision-making

Could AI help a child understand a care decision without steering the family unfairly?

Children with evolving decisional capacity; no participant sampleCC BY 4.0Generative AI / pediatric-consent ethicsFamily implications
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26

Vol. 1 · Issue 2 · Respect · Structured multi-model prompt experiment

Same child, different risk: demographic bias in childhood obesity attribution by large language models

Would an AI answer change if the same child were described with a different demographic background?

Pediatric obesity-risk scenarios; no child participantsCC BY 4.0Generative AI / demographic-bias experimentFamily implications
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27

Vol. 1 · Issue 2 · Respect · Qualitative caregiver and clinician interview study

Caregiver and Pediatric Clinician Perspectives on Artificial Intelligence for Language Services

What should a family be able to expect from AI translation or interpretation in a child’s care?

Pediatric patients and their caregivers; child ages varyCC BY 4.0AI language technologies / stakeholder perspectivesDirect parenting evidence
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28

Vol. 1 · Issue 2 · Respect · Qualitative adolescent and caregiver interview study

What patients and caregivers want to know when consenting to the use of digital behavioral markers

What should a teenager and parent know before AI infers health patterns from everyday data?

Adolescent patients and their adult caregiversCC BY 4.0AI-derived digital behavioral markers / consent studyDirect parenting evidence
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29

Vol. 1 · Issue 2 · Respect · Child-rights and regulatory analysis

Toy story or children story? Putting children and their rights at the forefront of the artificial intelligence revolution

What rights and risks should be considered before a connected toy enters family life?

Children using smart connected toys; no participant sampleCC BY 4.0Smart connected toys / AI-and-rights analysisFamily implications
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31

Vol. 1 · Issue 2 · Respect · Technical privacy-transformation and classifier evaluation

Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections

What can privacy edits protect, and what may they change in a child-video AI assessment?

Children in autism-related home videosCC BY 4.0Machine learning / privacy-preserving child-video evaluationFamily implications
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32

Vol. 1 · Issue 2 · Respect · Governance viewpoint with a longitudinal documentary case

Pediatric Clinical Images Without Consent: A Governance Gap in the Long-Term Reuse of Health Data in Digital Health Ecosystems

What happens to a child’s image after its original educational or clinical use?

Pediatric images and long-term reuse; no new child interventionCC BY 4.0AI training-data governance / case-based analysisFamily implications
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33

Vol. 1 · Issue 2 · Respect · Multisite qualitative parent and professional interview study

Health Care Professionals’ and Parents’ Perspectives on the Use of AI for Pain Monitoring in the Neonatal Intensive Care Unit: Multisite Qualitative Study

How can a parent’s role be respected when AI is proposed to monitor an infant’s pain?

Preterm infants; mean infant age 43 days in the parent sampleCC BY 4.0AI pain-monitoring concepts / qualitative perspectivesDirect parenting evidence
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34

Vol. 1 · Issue 2 · Respect · Cross-sectional caregiver survey

Integrating Artificial Intelligence in Pediatric Healthcare: Parental Perceptions and Ethical Implications

What information and human involvement do caregivers expect before AI is used in a child’s care?

Caregivers of pediatric patients; child ages varyCC BY 4.0Mixed clinical AI applications / caregiver surveyDirect parenting evidence
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36

Vol. 1 · Issue 2 · Respect · Qualitative parent and professional focus-group study

Feasibility and Acceptability of AI-Powered Tools for Early Autism Screening in Egypt: Semistructured Focus Group Study

What would make an AI screening tool acceptable and fair for families in different communities?

Children being considered for early autism screening; ages varyCC BY 4.0AI screening concepts / parent–professional perspectivesDirect parenting evidence
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37

Vol. 1 · Issue 2 · Respect · Cross-sectional child survey

Impact of artificial intelligence-generated self-images on children's body image development: a cross-sectional study in Mexico

How might AI-generated self-images relate to the way children feel about their bodies?

Children aged 6–12 yearsCC BY 4.0Generative AI self-images / observational studyChild-facing evidence
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38

Vol. 1 · Issue 2 · Respect · Participatory framework with three robot-design case studies

Designing for culturally responsive social robots: An application of a participatory framework

Whose culture and language are represented when an AI-like learning companion is designed?

Multilingual children in educational settings; ages vary across casesCC BY (version not stated)Social robot co-design / culturally responsive frameworkChild-facing evidence
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39

Vol. 1 · Issue 2 · Respect · Systematic review with risk-of-bias assessment

Artificial Intelligence and Child Abuse and Neglect: A Systematic Review

How reliable is the evidence behind AI systems intended to identify child abuse or neglect?

Child abuse and neglect datasets; ages varyCC BY 4.0Mixed predictive AI / systematic evidence reviewFamily implications
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40

Vol. 1 · Issue 2 · Respect · Cross-sectional adult stakeholder survey focused on child technologies

Emotional AI in education and toys: Investigating moral risk awareness in the acceptance of AI technologies from a cross-sectional survey of the Japanese population

How do people weigh privacy and emotional monitoring when AI is aimed at children?

Children’s education and toys; respondents were adults aged 20–69CC BY 4.0Emotional AI / child-technology stakeholder surveyFamily implications
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Study designs and age labels describe the source. Read each study’s findings and limitations before applying them to your family.