Author: Nidhi Choudhary
Co-author: Jasdeep Kaur
Lovely Professional University, Punjab
ABSTRACT
This paper examines the intersection between juvenile justice systems and artificial intelligence (AI), exploring how algorithmic tools risk-assessment instruments, decision-support systems, predictive analytics, surveillance technologies and digitally delivered rehabilitative programs are being introduced or contemplated in juvenile processes. It highlights potential benefits (personalized rehabilitation, efficient resource allocation) and the acute risks for children (bias, stigmatization, privacy intrusions, procedural unfairness). Anchored in a comparative legal review (India, EU, US) and international child-rights instruments, the paper lays out normative principles and operational safeguards (human-in-the-loop, algorithmic impact assessments, data minimization, explainability and contestability) tailored for juvenile justice. The central claim is that AI can be a constructive tool in juvenile systems only when accompanied by child-sensitive governance frameworks that prioritize the best interests of the child, due process, and rehabilitation over efficiency alone.
Keywords: Juvenile justice; artificial intelligence; children’s rights; algorithmic bias; rehabilitation; Juvenile Justice (Care and Protection of Children) Act, 2015; transparency; human oversight.
1. INTRODUCTION
Modern juvenile justice systems differ in principle and practice from adult criminal justice because they emphasize rehabilitation, reintegration and the developmental needs of children who come into conflict with the law. At the same time, public administrations and courts increasingly look to data and automated systems for efficiency, consistency and risk management. AI promises tailored interventions, early identification of vulnerabilities, and smarter resource allocation attractive goals where budgets and specialized personnel are limited. However, the use of algorithmic tools with children raises acute legal and ethical concerns: children are developmentally distinct, they enjoy special protections under international law, and decisions about their liberty and future demand particular procedural safeguards. This paper asks: can AI be designed and governed so that it supports the rehabilitative aims of juvenile justice rather than undermining child rights? The analysis proceeds by mapping law and policy, examining empirical lessons from adult criminal justice tools, and proposing a rights-based governance model for juvenile AI.
2. METHODOLOGY AND RESEARCH DESIGN
This paper is an interdisciplinary doctrinal and policy-oriented study that synthesizes legal analysis, comparative policy review, and empirical literature. The methodology comprises:
- Doctrinal legal review of national statutes and procedural rules (with a focus on India’s Juvenile Justice (Care and Protection of Children) Act, 2015) and international instruments (UNCRC).
- Policy synthesis of AI governance frameworks (UNESCO Recommendation on the Ethics of AI; EU AI Act; national approaches such as India’s DPDP Act and NITI Aayog Responsible AI guidance).
- Case and literature review of empirical and investigative studies on algorithmic tools in justice systems (notably ProPublica’s COMPAS analysis) and jurisprudence (State v. Loomis) that illuminate transparency and bias concerns.
- Normative analysis to derive operational recommendations attuned to child-rights norms (best interests, privacy, rehabilitation). The paper focuses on India but uses comparative examples from the EU and US as instructive contrasts.
Limitations: empirical studies measuring AI-driven interventions specifically on juveniles are sparse; much of the evidence is derived from adult criminal justice contexts and soft-law governance documents, requiring cautious application to juvenile settings.
3. LEGAL AND POLICY FRAMEWORKS
3.1 International Instruments: Rights of the Child and AI Ethics
The UN Convention on the Rights of the Child (UNCRC) provides core principles that must govern any technological intervention affecting children: the best interests of the child (Article 3), the right to privacy (Article 16), and the right to rehabilitation and fair treatment for children in conflict with the law (Articles 37 and 40)[1]. These rights imply higher thresholds for interventions that affect children’s liberty, dignity and prospects.
At the global governance level, UNESCO’s Recommendation on the Ethics of Artificial Intelligence (adopted in 2021) sets out principle’s human rights, justice, fairness, transparency, explainability and accountability that are directly applicable to AI used in sensitive public functions, including juvenile justice[2]. The Recommendation urges member states to conduct human rights impact assessments and to ensure that AI systems affecting fundamental rights are subject to appropriate oversight.
3.2 India: Juvenile Justice Act, DPDP Act, and Responsible AI Guidance
India’s Juvenile Justice (Care and Protection of Children) Act, 2015 (JJ Act) establishes specialized bodies (Juvenile Justice Boards, Child Welfare Committees) and prioritizes rehabilitation, restoration and non-institutional measures for children in conflict with the law. The JJ Act’s procedural emphasis on child-friendly processes and reintegration creates a statutory baseline that any AI application must respect decisions should remain individualized, rehabilitative and rights-oriented.
On data protection, the Digital Personal Data Protection Act, 2023 (DPDP) in India creates baseline rules for processing personal data, placing obligations on data fiduciaries regarding lawful processing, purpose limitation and data subject rights. While DPDP provides a privacy framework for personal data, it does not currently contain child-specific AI governance rules for justice settings, leaving a regulatory gap if AI becomes widely used in juvenile processes[3].
NITI Aayog and other Indian policy bodies have also produced Responsible AI guidance and white papers advocating principles such as fairness, transparency, privacy and human oversight useful starting points for India’s domestic governance of AI in public services[4].
3.3 EU & Comparative Approaches
The EU’s AI Act takes a risk-based regulatory approach, designating certain uses of AI (including those affecting fundamental rights) as “high risk” and placing stricter obligations on providers and deployers: transparency, documentation, quality of datasets, human oversight and conformity assessments. This prescriptive model is instructive for juvenile justice because it creates legal duties for high-impact AI systems and embeds procedural safeguards.
In the United States, AI governance is more fragmented; courts have begun to grapple with the use of proprietary risk scores in sentencing (the Wisconsin Loomis case[5]), and legislative experiments exist at state and local levels. The US experience underscores litigative and transparency challenges when black-box tools are used in liberty-affecting contexts.
4. TYPOLOGY OF AI APPLICATIONS IN JUVENILE JUSTICE
AI systems that could be used within juvenile justice processes broadly fall into five groups:
- Risk-assessment tools that predict recidivism or supervision risk to guide diversion, placement, or confinement decisions.
- Decision-support and recommendation systems for social workers and Juvenile Justice Boards (e.g., suggesting rehabilitation programs, educational placements, counselling needs).
- Predictive policing and hotspot analytics used by law enforcement to allocate resources or identify at-risk youth clusters.[6]
- Digitally delivered rehabilitation edtech, therapeutic chatbots, tele-counselling, and personalized learning modules.
- Automated monitoring and electronic supervision (e-tagging, geofencing, automated compliance tracking). [7]
Each category raises distinctive questions: risk tools more directly affect liberty and therefore demand the strictest governance; recommender systems for services may be lower risk if used only as non-binding aids; predictive policing and surveillance pose grave privacy and disproportionate-impact concerns for minors. The risk classification should inform proportional legal safeguards.
5. BENEFITS AND PROMISES OF AI FOR JUVENILE REHABILITATION
When designed and governed appropriately, AI can bring concrete benefits in juvenile contexts:
- Personalized rehabilitation: AI-driven assessments can help match youths to individualized education, therapeutic or vocational programs that reflect their strengths and needs rather than one-size-fits-all placements.
- Efficient resource allocation: predictive analytics may identify areas or cohorts in urgent need of psychosocial services or early interventions, enabling scarce social-work resources to be prioritized.
- Early identification of vulnerabilities: models trained to detect risk factors (school dropout indicators, repeated truancy, family stressors) can trigger protective interventions before offending escalates.
- Administrative simplification: automation can free social workers from routine paperwork, allowing more time for face-to-face rehabilitation work.[8]
These benefits are contingent on child-centered dataset design, participatory deployment, and strict privacy protections. Without those, promised efficiencies may translate into harms.
6. RISKS AND HARMS SPECIFIC TO CHILDREN
Children are uniquely vulnerable to several algorithmic harms:
6.1 Algorithmic Bias and Disparate Impact
Historical data arrests, convictions, school discipline often reflect structural inequalities (poverty, caste/class, race, neighbourhood policing practices). Models trained on such data can reproduce and amplify those inequalities, mislabelling marginalized children as high risk and subjecting them to stricter supervision or institutionalization. Empirical analyses in adult contexts (e.g., ProPublica’s COMPAS study) illustrate the risk of group disparities even when overall accuracy seems acceptable.
6.2 Stigmatization and Labelling Effects
Risk scores or surveillance records can create persistent labels that follow a child into education, employment or community settings, impeding reintegration and psychological development. The formative years are sensitive to stigma; algorithmic classifications risk hardening social exclusion.[9]
6.3 Privacy & Surveillance Harms
Surveillance technologies (facial recognition, location tracking) can normalize invasive monitoring, chill normative teenage behaviour and disrupt trust between children, social workers and families. Children have heightened privacy rights under the UNCRC; constant monitoring is antithetical to their development and dignity.
6.4 Procedural Fairness & Contestability
Opaque or proprietary systems used in quasi-judicial decisions threaten the child’s ability to know and contest evidence used against them. The Loomis litigation exemplifies how courts struggle with admissibility, disclosure and the balance of proprietary secrecy with procedural fairness. [10]
6.5 Lack of Evidence & Overreliance on Technology
There is limited robust evidence that algorithmic tools used in criminal justice materially improve rehabilitative outcomes for juveniles. Overreliance on tools without randomized or controlled field evaluations risks instituting systemic errors at scale.[11]
Given these risks, a cautious, rights-first approach is necessary for juvenile AI deployment.
7. CASE STUDIES & EMPIRICAL EVIDENCE
7.1 COMPAS and ProPublica (United States)
ProPublica’s 2016 analysis of COMPAS (a commercial recidivism-prediction tool) found that black defendants were more likely than white defendants to be misclassified as high risk for recidivism, whereas white defendants were more likely to be misclassified as low risk. While COMPAS is an adult criminal justice tool, its controversy is instructive: it revealed how opaque scoring systems trained on biased data can have racially disparate effects and highlighted the difficulty of reconciling different fairness metrics. Policymakers should heed these lessons before deploying similar scoring tools in juvenile contexts. [12]
7.2 State v. Loomis (Wisconsin Supreme Court)
In State v. Loomis (2016)[13], the Wisconsin Supreme Court allowed limited use of an actuarial risk assessment (COMPAS) in sentencing but cautioned against overreliance on proprietary algorithms that cannot be meaningfully examined by defendants and warned judges to consider the tool’s limitations. Loomis illustrates the judicial struggles around transparency, the right to meaningful explanation, and the tension between proprietary trade secrets and procedural fairness. This jurisprudence is directly relevant for juvenile justice boards and courts considering algorithmic evidence.
7.3 EU Regulatory Development & National Laws (Illustrative)
The EU AI Act’s risk-based approach (and national initiatives such as Italy’s recent comprehensive AI law) shows an international trend toward stricter governance of high-impact AI systems, including those used in law enforcement and judicial contexts. The EU’s approach mandates documentation, transparency, human oversight and conformity assessments for high-risk systems a model that could be adapted to juvenile justice to ensure procedural safeguards and third-party auditing. [14]
8. RIGHTS-BASED GOVERNANCE: PRINCIPLES AND RULES FOR JUVENILE AI
Any framework regulating AI in juvenile justice must be anchored in core rights and principles:
- Best interests of the child (UNCRC Article 3): AI deployments must demonstrably serve rehabilitative aims and not merely administrative convenience.
- Proportionality and necessity: Liberty-affecting algorithmic uses must be necessary, proportionate and subjected to the least intrusive means test[15].
- Transparency and contestability: Children and their guardians must be able to understand and contest algorithmically influenced decisions, with meaningful explanations provided. The Loomis cautions about black-box systems.
- Human oversight (human-in-the-loop): Humans with domain expertise must retain decision-making authority and the power to override algorithmic outputs, recording reasons for any overrides[16].
- Data minimization & purpose limitation: Collect only what is necessary for rehabilitative objectives; avoid indefinite retention of sensitive data.
- Independent auditability and public reporting: Third-party audits and disaggregated outcome reporting (by age, gender, caste, socioeconomic status) are required to detect divergent impacts.
- Participatory design & rights literacy: Engage children, caregivers, social workers and civil society in design and oversight; offer legal aid and AI literacy materials to affected children. [17]
9. TECHNICAL AND PROCEDURAL SAFEGUARDS
9.1 Algorithmic Impact Assessments (AIA) for Minors
Before deployment, any system that materially affects a child should undergo a publicly available AIA assessing fairness risks, data provenance, developmental harms and remedial pathways. The AIA should involve child-rights experts, psychologists and community representatives. UNESCO and other bodies endorse impact assessments as governance tools.
9.2 Model Choice, Explainability and Interpretability
Prefer interpretable models over complex black-box systems in liberty-affecting decisions. When complex models are used, provide local, case-specific explanations: what input factors influenced the score, how sensitive the prediction is to plausible data changes, and what non-algorithmic evidence supports or contradicts the output. [18]
9.3 Data Governance and Privacy Preservation
Adopt data minimization, retention limits and privacy-preserving methods (differential privacy, secure multi-party computation, federated learning) to reduce risks of data leaks and secondary uses of child data. The DPDP Act supplies a national frame for personal data; juvenile systems should adopt heightened protections for minors.
9.4 Human-Centered Deployment & Overrides
Create statutory or procedural rules requiring that algorithmic recommendations are non-binding in liberty-affecting decisions and that human decision-makers document reasons when they follow or override algorithmic suggestions. This creates accountability and prevents blind automation.
9.5 Auditing, Monitoring and Redress
Mandate regular external audits for bias and disparate impact; require public reporting of outcomes. Provide accessible channels for children and guardians to challenge algorithm-driven decisions and obtain remedies, including independent oversight by child-rights bodies[19].
10. ROADMAP FOR IMPLEMENTATION IN INDIA
To harness AI safely within juvenile justice while safeguarding child rights, India can adopt a phased roadmap:
- Policy clarification & moratorium on high-risk uses: Immediately prohibit liberty-depriving decisions based solely on algorithmic scores and restrict facial recognition and surveillance of minors pending regulatory safeguards.
- Draft juvenile-AI guidelines: The Ministry of Women & Child Development (MWCD), in consultation with the Ministry of Law & Justice, MeitY and NITI Aayog, should draft child-centric AI guidelines that define permitted uses, required AIAs, and audit cycles.[20]
- Pilot projects in non-adversarial contexts: Start with decision-support pilots (resource allocation, education planning) that are non-binding and fully evaluated via independent studies.
- Capacity building: Train Juvenile Justice Board members, social workers and prosecutors in AI literacy, bias awareness, and contestation mechanisms.[21]
- Legal reform: Amend rules under the JJ Act or introduce subordinate rules requiring transparency and AIAs for technologies used within juvenile processes; integrate child-specific clauses into DPDP or issue child privacy regulations.[22]
11. COMPARATIVE ANALYSIS: INDIA VS EU VS US
- India: Strong statutory focus on rehabilitation in the JJ Act but lacks explicit AI governance in juvenile contexts; DPDP offers privacy baseline but not exhaustive child-safeguards. India’s policy documents (NITI Aayog) provide principles but no binding obligations. A tailored regulatory intervention is required to bridge statutory and technological gaps. [23]
- EU: The AI Act creates a prescriptive, risk-based legal order for high-impact systems, with detailed obligations on transparency, conformity and human oversight. This model offers concrete tools (conformity assessments, registered systems) that could be adapted for juvenile contexts to ensure rigorous governance. [24]
- US: Litigation (e.g., Loomis) and investigative journalism (ProPublica) have driven public debate; however, regulatory fragmentation leaves gaps. The US experience emphasizes judicial scrutiny and public controversy, signalling the importance of pre-emptive regulation rather than reactive litigation[25].
12. RECOMMENDATIONS & POLICY PROPOSALS
The following policy recommendations target policymakers, juvenile justice administrators, technologists and civil society.
12.1 Legal & Regulatory Measures (Statutory & Subordinate Rules)
- Amend juvenile procedure rules to require express statutory safeguards for any algorithmic system used in juvenile processes, including mandatory AIAs, rights to explanation, and non-binding status of algorithmic recommendations.
- Child-specific data protections: Adopt DPDP implementing rules or MWCD guidelines that impose stricter processing limits for child data in justice contexts (shorter retention, strict purpose limitation, prohibition on profiling for punitive ends).
12.2 Administrative & Institutional Reforms
- Create oversight bodies or extend the mandate of existing child-rights institutions to oversee AI deployments in juvenile justice, with auditing powers and the ability to order suspensions.
- Institutionalize AI Literacy & Legal Aid for children and caregivers to make contestation meaningful.
12.3 Technical & Procurement Standards
- Procurement safeguards: Government procurement for AI systems must require open documentation, independent audits and local explainability guarantees. Open-source and interpretable models should be preferred for high-impact uses.
- Vendor accountability: Require suppliers to provide evidence of dataset provenance, fairness testing and to submit to independent conformity audits.
12.4 Research & Evidence Building
- Fund randomized field trials and longitudinal studies measuring rehabilitative outcomes where AI tools are used, comparing to control interventions. Avoid large-scale deployment until positive outcomes are robustly demonstrated.
- Data sharing for independent research: Facilitate anonymized data access under controlled conditions for independent evaluation while protecting privacy.
12.5 Ethical & Human Rights Safeguards
- Ban/limit surveillance of minors with clear exceptions for immediate safety with judicial oversight.
- Redress & remedy: Ensure prompt and child-friendly mechanisms to contest algorithm-informed decisions and obtain corrections.
13. CONCLUSION
Artificial intelligence offers promising tools that if carefully designed and vigilantly governed can support the rehabilitative mission of juvenile justice systems by personalizing interventions and improving service allocation. Yet, children’s developmental vulnerability, legal rights under the UNCRC, and empirical lessons from adult criminal justice require a cautious, rights-centered approach. The principal takeaway is that algorithmic tools must not shortcut the normative and procedural protections central to juvenile justice: transparency, contestability, human oversight, and the primacy of the child’s best interests.
For India, the path forward includes drafting child-centric AI guidelines, piloting low-risk decision aids, embedding AIAs and audits in procurement and operations, strengthening DPDP child protections, and prioritizing empirical evaluation before the technology touches liberty-affecting decisions. International regulatory developments (EU AI Act) and normative instruments (UNESCO Recommendation, UNCRC) provide useful scaffolding; Indian policymakers must adapt those lessons to the country’s juvenile-law objectives and social realities.
Ultimately, the deployment of AI in juvenile justice should be governed by the question: does the technology help the child to thrive or does it threaten the child’s future through opaque classification and surveillance? The answer will determine whether algorithms become tools of rehabilitation and protection or instruments that deepen inequality and curtail young lives.
14. REFERENCES
- Juvenile Justice (Care and Protection of Children) Act, 2015 (India), available at Government of India, IndiaCode (text and PDF).India Code
- Convention on the Rights of the Child, 1989 (UN).OHCHR
- UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021).UNESCO
- Jeff Larson, Surya Mattu, Lauren Kirchner & Julia Angwin, How We Analyzed the COMPAS Recidivism Algorithm, ProPublica (23 May 2016).ProPublica
- State v. Loomis, 2016 WI 68, Supreme Court of Wisconsin (13 July 2016).wicourts.gov
- European Commission / EU AI Act High level summaries and legislative text (see EU Parliament topics and related official documents).European Parliament+1
- Digital Personal Data Protection Act, 2023 (India), Ministry of Electronics & IT (MeitY), Government of India.MeitY
- NITI Aayog / Government of India, Responsible AI Approach Document/White Paper (Responsible AI for All / Responsible AI usage guide).Niti Aayog\
- Additional news/regulatory developments referenced: Italy’s AI law (The Guardian), DPDP rules release reporting (Economic Times).
[1] Convention on the Rights of the Child, 20 Nov. 1989, United Nations, arts. 3, 16, 37 & 40.
[2] UNESCO, Recommendation on the Ethics of Artificial Intelligence (Adopted 2021).
[3] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[4] NITI Aayog (Govt. of India), Responsible AI for All: Approach Document (Feb. 2021).
[5] State v. Loomis, 881 N.W.2d 749 (Wis. 2016).
[6] AI Act (Risk-based regulatory framework proposals and summaries).
[7] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[8] Juvenile Justice (Care and Protection of Children) Act, 2015 (India), s.1 et seq. (text available at IndiaCode PDF).
[9] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[10] AI Act (Risk-based regulatory framework proposals and summaries).
[11] J. Larson et al., How We Analyzed the COMPAS Recidivism Algorithm, ProPublica (23 May 2016).
[12] UNESCO, Recommendation on the Ethics of Artificial Intelligence (Adopted 2021).
[13] State v. Loomis, 881 N.W.2d 749 (Wis. 2016).
[14] European Union, Regulation (EU) — AI Act (Risk-based regulatory framework proposals and summaries).
[15] J. Larson et al., How We Analyzed the COMPAS Recidivism Algorithm, ProPublica (23 May 2016).
[16] UNESCO, Recommendation on the Ethics of Artificial Intelligence (Adopted 2021).
[17] Convention on the Rights of the Child, 20 Nov. 1989, United Nations, arts. 3, 16, 37 & 40.
[18] Juvenile Justice (Care and Protection of Children) Act, 2015 (India), s.1 et seq. (text available at IndiaCode PDF).
[19] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[20] J. Larson et al., How We Analyzed the COMPAS Recidivism Algorithm, ProPublica (23 May 2016).
[21] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[22] NITI Aayog (Govt. of India), Responsible AI for All: Approach Document (Feb. 2021).
[23] The Digital Personal Data Protection Act, No. 22 of 2023 (India).
[24] European Union, Regulation (EU) AI Act (Risk-based regulatory framework proposals and summaries).
[25] State v. Loomis, 881 N.W.2d 749 (Wis. 2016).
