Project Charter: AI-Enhanced Child Helpline System
1. Project Overview
Project Name: AI-Enhanced Child Helpline System
Project Background:
The AI-Enhanced Child Helpline System project is initiated following the successful award of funding from the Venture Fund, focused on advancing open-source technology solutions to support child protection services. The primary goal is to leverage artificial intelligence to improve the identification and response to cases of Violence Against Children (VAC) and Gender-Based Violence (GBV) during helpline calls. By addressing the critical need for timely and accurate case identification, this project aims to enhance the capacity of child helplines to respond effectively and safeguard vulnerable children.
Project Purpose:
The purpose of this project is to develop and deploy an integrated AI-driven solution that assists child helpline operators by automating case triage, transcription, and translation. This will enable quicker identification of VAC and GBV cases, reduce response times, and enhance the overall intervention outcomes. The project aligns with UNICEF’s mission to leverage innovative technology to protect children, particularly those in marginalised and underserved communities. By making the solution open-source, the project aims to facilitate broader adoption, enabling similar organisations to benefit from enhanced child protection capabilities.
Key Stakeholders
- UNICEF: Providing funding, guidance, and alignment with child protection objectives.
- Development Team: Responsible for designing, developing, and testing the AI solution.
- Target User Groups: Child helpline operators, case managers, and support staff who will use the AI tool to enhance service delivery.
- Child Protection Organizations: Collaborating entities that will help pilot the solution and provide feedback for refinement.
- Open-Source Community: Contributors who will support the development, testing, and documentation of the project to ensure its sustainability and scalability.
2. Project Objectives
- Improve Response to VAC and GBV Cases: Implement AI-driven tools to assist child helpline operators in accurately identifying cases of Violence Against Children (VAC) and Gender-Based Violence (GBV) during real-time calls. Enhance service delivery through automated transcription, translation, and prediction models, leading to faster case triage and more effective interventions.
- Enhance Data Management for Child Helplines: Provide a robust data management system that enables real-time analysis and reporting, helping helplines track cases, identify trends, and make data-driven decisions. Optimize data processing pipelines to ensure accurate handling of case information, supporting efficient case management.
- Foster Collaboration and Data Sharing Among Stakeholders: Develop and release the solution as an open-source project to encourage widespread adoption and collaboration among child helplines, NGOs, and other stakeholders. Facilitate data sharing and integration with existing helpline systems, promoting a unified approach to handling and reporting VAC and GBV cases.
- Implement AI for Efficient Case Management: Leverage AI technologies such as Natural Language Processing (NLP) and machine learning to streamline case assessment, prioritise urgent cases, and provide recommendations for appropriate interventions. Continuously refine the AI models based on real-world feedback to improve accuracy and reduce response times.
- Provide Real-Time Data Analysis and Reporting Tools: Integrate real-time data analysis features that allow helpline operators and stakeholders to monitor call statistics, identify emerging patterns, and generate reports on case outcomes. Enhance reporting capabilities to provide actionable insights that can guide policy decisions and resource allocation.
3. Scope of Work
In-Scope
The following features, functionalities, and deliverables will be developed as part of the project:
Frontend and Backend Features:
- User Interface Development: Design and implement an intuitive, user-friendly interface for child helpline operators, enabling efficient case management and data input.
- Voice Transcription and Translation Pipeline: Develop and integrate a voice transcription module to convert call audio into text in real-time. Implement a translation module to handle multilingual inputs, ensuring accessibility across different language groups.
- Case Prediction and Classification Model: Create and refine a machine learning model to predict and classify VAC and GBV cases based on call data, providing recommendations for appropriate interventions.
- Data Management and Reporting: Build a secure data storage and management system that can handle large volumes of call data. Implement real-time analytics and reporting tools for case monitoring, trend analysis, and generating actionable insights.
Open-Source Requirements:
- Repository Setup: Establish a public repository following open-source standards with proper version control.
- Documentation: Develop comprehensive user and developer documentation, including setup guides, usage instructions, and API references. Provide clear contributing guidelines to encourage community participation and contributions.
- Community Engagement: Organize workshops or webinars to onboard contributors and provide ongoing support for community engagement. Incorporate feedback and contributions from the open-source community to enhance the project.
Quality Assurance and Testing Coverage:
- Unit Testing and Integration Testing: Achieve at least 80% test coverage across the codebase, focusing on key features such as transcription, translation, and prediction accuracy.
- Usability Testing: Conduct user testing sessions to evaluate the product's functionality and user experience.
- Security and Privacy Audits: Perform audits to ensure compliance with privacy regulations, particularly regarding the handling of sensitive PII data.
Deployment and Maintenance:
- Initial Deployment: Deploy the integrated system on a cloud platform for pilot testing.
- Ongoing Maintenance: Provide regular updates, bug fixes, and performance improvements during the project period.
Out-of-Scope
The following aspects are not included in the current project phase:
- Integration with External Helpline Systems: While data import/export features may be provided, direct integration with existing helpline systems is not part of the initial scope.
- Advanced Predictive Analytics Beyond VAC/GBV Cases: The initial focus will be on VAC and GBV case prediction. Expanding the model to cover other case types or broader predictive analytics will be considered in future phases.
- Comprehensive User Training Programs: While basic training will be provided for pilot users, full-scale training programs for end-users across all potential helplines are not included in this phase.
- Model Deployment & Continued Support: The scope of this project is restricted to the development and testing of AI models for voice recognition/transcription, language translation, case prediction, and data science. Collaboration with helplines and pertinent government entities will be necessary regarding deployment and ongoing support.
- Offline Functionality: The application will be designed for online use only, relying on cloud-based processing. Offline data processing or local deployment options are not planned for this phase.
4. Project Deliverables
The project is divided into phases, with key deliverables planned for each quarter. These include software modules, documentation, testing, and community engagement activities.
Quarter 1: Planning and Initial Development
- Needs Assessment and Stakeholder Consultation: Conduct comprehensive discussions with child helplines and relevant stakeholders to gather detailed requirements. Finalise the project plan, outlining timelines, resources, and key performance indicators (KPIs).
- Data Collection and Preparation: Collect and preprocess historical call data for use in model training. Set up the data schema and establish privacy and security protocols for handling sensitive information.
- Initial System Architecture Design: Design the architecture for the integrated system, covering frontend, backend, and machine learning pipelines. Prepare initial repository setup, including version control, basic documentation, and contributing guidelines.
Quarter 2: Core Model Development and Testing
- Voice Transcription and Translation Modules: Develop and implement a voice-to-text transcription module with support for multiple languages. Integrate a translation module to handle multilingual inputs for wider accessibility.
- Case Prediction Model: Train an initial machine learning model to predict and classify VAC/GBV cases based on call data. Conduct unit testing and achieve at least 60% test coverage for these modules.
- User Interface (UI) Prototype: Develop a prototype UI for the child helpline operators, incorporating feedback from stakeholders during iterative testing.
- Community Engagement Kickoff: Host a webinar to introduce the project to the open-source community, inviting contributions and feedback.
Quarter 3: Integration, Optimization, and Pilot Testing
- System Integration: Integrate the transcription, translation, and prediction models into a unified system. Optimize data flow between modules to ensure seamless operation and low latency.
- Quality Assurance and Testing: Conduct extensive integration testing, focusing on performance and user experience. Achieve 80% code test coverage, including unit, integration, and usability testing.
- Pilot Testing with Child Helplines: Deploy the integrated system for pilot testing with selected child helplines. Gather feedback from users on system performance, usability, and case prediction accuracy.
- Open-Source Documentation and Community Contributions: Update the public repository with detailed documentation and guidelines for contributors. Facilitate a code review session with the open-source community to gather feedback and incorporate improvements.
Quarter 4: Full-Scale Evaluation, Refinement, and Dissemination
- User Feedback Analysis and System Refinement: Analyze feedback from pilot testing to identify areas for improvement. Refine the models and system based on user inputs and performance metrics.
- AI System Usability Evaluation: Conduct A/B testing to compare the AI-assisted system with traditional processes, focusing on metrics like case identification accuracy and response times. Implement UI/UX improvements based on user feedback.
- Final Documentation and Open-Source Compliance: Complete comprehensive user and developer documentation, ensuring compliance with open-source standards. Conduct a final quality assurance review, achieving at least 80% test coverage and passing privacy audits.
- Project Findings Dissemination: Prepare and publish reports on project outcomes, including case studies and performance metrics. Present the findings at relevant conferences and workshops, aiming to foster adoption by at least three new child helplines.
- Sustainability and Handover Plan: Develop a sustainability plan outlining the next steps for maintenance, community engagement, and potential scaling of the solution. Handover the system to the designated maintenance team, including a final review with stakeholders and an exit report.
5. Milestones and Timeline
Quarter 1
| Milestone | Activity | Month(s) |
|---|---|---|
| Milestone 1: Project Kick-off, Planning, and Research | Activity 1: Project Kickoff (Establish project framework, scope, and align stakeholders) | Month 1 |
| Activity 2: Team Formation and Training (Build and train project team) | Month 1 | |
| Activity 3: Technical Infrastructure Setup (Establish infrastructure, plan resources) | Month 2 | |
| Milestone 2: Validation and Feedback Activities | Activity 1: Infrastructure Validation and Testing (Stress, security, and integration tests) | Month 2 |
| Activity 2: Privacy & Security Awareness (Conduct awareness sessions, Privacy Impact Assessment) | Month 2 | |
| Milestone 3: Data Acquisition and Exploration | Formalise data acquisition process through MoUs with helplines and governments | Month 2-3 |
| Milestone 4: Cohort Product Development | Activity 1: Attend Technical Product Development Session | Month 3 |
| Activity 2: Revision of Work Plan at End of Quarter 1 | Month 3 | |
| Milestone 5: Capacity Building | Workshops on data preprocessing, ML model development, and privacy best practices | Month 3 |
| Milestone 6: Data Privacy & Protection | Implement data privacy policies, anonymize sensitive info, and ensure access control | Month 3 |
| Milestone 7: Communications and Branding | Share monthly project updates on social media, publish blog posts | Ongoing |
| Milestone 8: Voice Data Preprocessing and Transcription Pipeline | Set up and test voice data preprocessing pipeline, begin transcription model training | Month 3-4 |
| Milestone 9: Meeting Open Source Contractual Requirements | Review licensing requirements, develop project charter, create READMEs for repositories | Month 2-3 |
Quarter 2
| Milestone | Activity | Month(s) |
|---|---|---|
| Milestone 1: Text Data Preprocessing and Normalisation | Clean and Preprocess Structured and Unstructured Text Data (Remove inconsistencies, duplicate entries) | Month 4-5 |
| Apply Text Normalisation Techniques (e.g., spelling correction, stemming) | Month 4-5 | |
| Develop Reusable Text Processing Components for Future Pipelines | Month 4-5 | |
| Document the Text Preprocessing Pipeline and Its Outputs (Comprehensive documentation) | Month 4-5 | |
| Milestone 2: Language Translation Pipeline | Develop a Machine Translation Model for Structured and Unstructured Data | Month 5-6 |
| Test Translation Accuracy Across Multiple Languages | Month 5-6 | |
| Integrate Translation Module into the Transcription Pipeline | Month 5-6 | |
| Identify and Document Key Translation Challenges and Improvements Needed | Month 5-6 | |
| Milestone 3: Validation and Feedback | User Testing (Product and UX) | Month 5-6 |
| User Testing (UI) | Month 5-6 | |
| Milestone 4: Communications and Branding | Share 1 image + caption per month on social media channels | Ongoing |
| Milestone 5: Capacity Building | Conduct Monthly Workshops (Data preprocessing, privacy and security best practices, ML development) | Month 4-6 |
| Milestone 6: Meeting Open Source Contractual Requirements | OSI-approved licence for public repositories | End of Q2 |
| Create contributing guidelines for Open Source repositories | End of Q2 | |
| Create public tickets/issues for features and bugs | End of Q2 | |
| Use a public project management board (e.g., Taiga, GitHub, JIRA) | End of Q2 | |
| Add developer/user documentation to Open Source site | End of Q2 | |
| Advance Open Source QA (15% code coverage for unit tests) | End of Q2 | |
| Milestone 7: Privacy & Security - Development | Develop privacy-specific agreements (Terms of Use, user consent notice, etc.) | Month 5-6 |
| Develop a privacy policy | Month 5-6 | |
| Develop a data retention policy | Month 5-6 | |
| Milestone 8: Cohort Product Development | Revision of the work plan at the end of Q2 | End of Q2 |
Quarter 3
| Milestone | Activity | Month(s) |
|---|---|---|
| Milestone 1: Case Prediction Model Development | Label Datasets for Case Prediction Tasks | Months 6-8 |
| Develop and Train the Prediction Model | Months 6-8 | |
| Test and Evaluate Model Performance (Precision, Recall, Accuracy) | Months 6-8 | |
| Iteratively Improve Model Based on Feedback and Real-World Data | Months 6-8 | |
| Milestone 2: Integration and Optimization | Integrate the Transcription, Translation, and Case Prediction Pipelines | Months 9-10 |
| Ensure Seamless Data Flow Between the Modules | Months 9-10 | |
| Test the Integrated System End-to-End and Identify Performance Bottlenecks | Months 9-10 | |
| Finalize Integration Documentation and Prepare for Deployment | Months 9-10 | |
| Milestone 3: User Pilot Testing | Validate the Integrated System (focusing on performance of case prediction, transcription, and translation) | Month 9 |
| Validate Performance in Real-World Scenarios, Ensure Data Flows Seamlessly | Month 9 | |
| Conduct Pilot Testing with Controlled User Groups (Helpline Testing) | Month 9 | |
| Evaluate Model Effectiveness and User Experience in Real-World Settings | Month 9 | |
| Validate UI/UX of the Integrated System to Ensure a User-Friendly Interface | Month 9 | |
| Final Fine-Tuning Based on Pilot Testing Results and Real-World Data | Month 9 | |
| Milestone 4: Communications and Branding | Share Image and Caption on Social Media | Monthly |
| Publish Blog Post at the Beginning of the Quarter | Quarterly | |
| Milestone 5: Capacity Building | Conduct Workshops on Advanced Data Preprocessing Techniques (cleaning, normalisation, labelling) | Monthly |
| Provide Training on Privacy and Security Best Practices (handling PII) | Monthly | |
| Hold Capacity-Building Sessions on ML Model Development (transcription, translation, prediction) | Monthly | |
| Facilitate Discussions on Ethical AI Practices and Responsible Use of AI Technologies | Monthly | |
| Milestone 6: Meeting Open Source Contractual Requirements | Set up Continuous Integration / Continuous Deployment (CI/CD) Pipeline | End of Q3 |
| Add Ticket/Issue Templates for Public and Core Contributor Team | End of Q3 | |
| Create "Good First Issues" for Low-Commitment Contributions | End of Q3 | |
| Add Developer/User Documentation to Open Source Documentation Site | End of Q3 | |
| Review Compliance Against DPG Criteria and Address Gaps | End of Q3 | |
| Milestone 7: Privacy & Security - Policy Development | Develop Documents for Privacy-Specific Language in Terms of Use, Vendor Contracts, and Consent Notices | End of Q3 |
| Develop Privacy Policy | End of Q3 | |
| Develop Data Retention Policy | End of Q3 | |
| Milestone 8: Cohort Product Development | Revise the Work Plan at the End of the Third Quarter | End of Q3 |
Quarter 4
| Milestone | Activity | Month(s) |
|---|---|---|
| Milestone 1: AI Model Performance Evaluation | Evaluate the Full-Scale Deployment of the Model (Effectiveness in child helplines, identifying VAC/GBV cases during calls) | Months 10-12 |
| Evaluate AI's Role in Reducing Response Times and Improving Intervention Outcomes | Months 10-12 | |
| Identify Whether AI Can Help Recognize Cases Before Calls End (A/B Testing) | Months 10-12 | |
| Compare AI-Assisted Interventions Against Traditional Methods (A/B Testing) | Months 10-12 | |
| Milestone 2: Project Findings Dissemination | Prepare Reports, Publications, and Conference Presentations to Share Findings | Months 10-12 |
| Engage Stakeholders to Refine the Model and Promote Broader Adoption | Months 10-12 | |
| Present Model Benefits and Adoption to Child Helplines and Related Organizations | Months 10-12 | |
| Aim for Replication in at Least Three Systems | Months 10-12 | |
| Milestone 3: AI System Usability Evaluation | Conduct User Testing (50-100 Users) to Evaluate Product Functionality and User Experience | Months 10-12 |
| Implement Changes Based on Feedback | Months 10-12 | |
| Run A/B Testing to Compare AI-Assisted System with Current Process for Key Metrics | Months 10-12 | |
| Milestone 4: User Interface Improvement | Conduct UI Testing to Ensure Intuitive Design, Collecting Feedback from 50-100 Users | Months 10-12 |
| Milestone 5: Communications and Branding | Share Image and Caption on Social Media Monthly to Raise Awareness and Engagement | Monthly |
| Publish Blog Post to Provide In-Depth Updates on Project Progress and Educate Stakeholders | Quarterly | |
| Milestone 6: Documentation & QA Compliance | Finalize Comprehensive User and Developer Documentation to Ensure Open-Source Compliance | End of Q4 |
| Milestone 7: Code Testing & Privacy Audits | Achieve at Least 80% Code Test Coverage and Ensure Privacy Compliance | End of Q4 |
| Milestone 8: Data Privacy Strategy Development | Review Data Schema for Accuracy and Compliance with Privacy Laws | End of Q4 |
| Develop a Data Map for Short-Term (One-Year) Forward Strategy | End of Q4 | |
| Ensure Sensitive PII Remains Protected in Compliance with Local and International Privacy Laws | End of Q4 | |
| Milestone 9: Meeting Open Source Contractual Requirement | Finalize Open Source Documentation | End of Q4 |
| Finalize Open Source Quality Assurance | End of Q4 | |
| Growth Planning, Contextual Analysis, and Focused Support with Open Source Mentor | End of Q4 | |
| Milestone 10: Cohort Product Development | Attend Virtual Graduation | End of Q4 |
| Initiate Submission for DPGA for Consideration | End of Q4 | |
| Work with Privacy and Security Mentor to Ensure Compliance with DPG Indicator 7 and 9a | End of Q4 | |
| Milestone 11: Data Processing Strategy Development | Review Data Schema to Ensure Data Processing Practices Are Accurate and Updated | End of Q4 |
| Determine Whether Current Data Processing Strategy Is Appropriate (Given Privacy & Data Protection Considerations) | End of Q4 | |
| Develop a Data Map for the Data Processing Strategy for the Short-Term (One-Year Forward) | End of Q4 |
6. Success Metrics
| Metric Area | Metric/KPI | Target |
|---|---|---|
| User Adoption and Engagement | User Adoption Rate | X% of helplines integrate AI-assisted interventions |
| Frequency of Use | Average number of cases processed per day by each helpline worker | |
| Engagement Metrics | X% increase in the usage of AI-assisted features compared to manual methods | |
| Call Center Metrics | prank/dropped/blank/silent | |
| Case Management Metrics | Resolution time | |
| Data Completeness | Accuracy | |
| AI Model Performance | Case Prediction Accuracy | Precision and recall rates: 85% precision, 80% recall (example) |
| Model Improvement Over Time | X% improvement in case identification accuracy over time | |
| Response Time Reduction | Reduce response times by X% through AI automation | |
| System Reliability and Uptime | System Availability/Uptime | Achieve 99.9% uptime for the system |
| Error Rate | Maintain an error rate of <X% in production | |
| User Experience and Satisfaction | User Feedback Scores | Achieve an average score of X/5 on user satisfaction |
| UI/UX Testing Results | Achieve a task completion rate of at least X% with no major usability issues | |
| Open Source Contribution | Contribution Metrics | Achieve X pull requests merged per month, contributions from at least X external contributors |
| Issue Resolution Rate | Resolve X% of open issues within one month of being raised | |
| Community Growth | Increase active contributors by X% over project duration | |
| Testing Coverage and Quality | Code Coverage | Achieve at least 80% code test coverage |
| Privacy and Security Audits | Pass privacy and security audits with no critical vulnerabilities | |
| Impact and Outcomes | Case Identification and Intervention Effectiveness | Achieve X% improvement in case handling effectiveness (e.g., arrests avoided, improved victim support) |
| Adoption by Helplines | At least X child helplines adopt the system within one year of deployment | |
| Reduction in Response Time | X% reduction in response times compared to pre-AI system benchmarks | |
| Compliance and Documentation | Documentation Completeness | Achieve 100% completeness for system and user documentation |
| Regulatory Compliance | Achieve full compliance with GDPR and other applicable privacy laws | |
| Branding and Communication | Social Media Engagement | Publish X social media posts per month with engagement rates of at least Y% |
| Stakeholder Feedback | Receive positive feedback from at least X stakeholders or organizations, with X% expressing interest in adopting or promoting the system |
7. Project Assumptions
| Assumption Area | Assumption | Justification/Impact |
|---|---|---|
| Resource Availability | Sufficient computing resources will be available for model training and deployment. | Without adequate infrastructure, model performance and scalability could be compromised. |
| Cloud storage and computing services will be provided as per agreements with cloud providers. | Ensures the project’s technical scalability and avoids resource constraints. | |
| Data Access | Access to diverse and ethically sourced data (calls, chats, reports) will be provided. | Essential for building and training the AI models to recognize VAC/GBV cases. |
| Data providers will cooperate in anonymizing sensitive data for privacy compliance. | Protecting sensitive data is crucial for legal compliance and user trust. | |
| Target Users | Key stakeholders (e.g., child helplines) will adopt the system and actively participate in testing. | Engagement from target users is necessary for product validation and deployment. |
| Pilot testing will be conducted with controlled user groups for accurate performance validation. | Real-world testing ensures the system is effective and provides actionable feedback. | |
| Funding and Budget | Sufficient funding will be secured to cover the entire project lifecycle. | Uninterrupted financial resources are critical for development, testing, and deployment phases. |
| Team members will be available for the project duration as per the recruitment plan. | The availability of skilled staff is necessary for the project’s success. | |
| Technology and Tools | Necessary software tools and libraries (e.g., NLP, AI models) will be available for integration. | Technology is fundamental for the development and integration of key system functionalities. |
| Open-source tools and frameworks will meet project requirements without major modifications. | Leveraging open-source tools can reduce costs and improve flexibility. | |
| Legal and Compliance | Regulatory bodies (e.g., GDPR) will provide clear guidelines for data processing and privacy. | Compliance with data protection regulations is crucial for avoiding legal risks. |
| Consent forms and privacy agreements will be signed by users and partners. | Legal compliance ensures ethical use of sensitive data and protects the project from litigation. | |
| Team Competency | The project team will possess the required technical and domain expertise (data science, AI, NLP, child protection, etc.). | A skilled team ensures that the project objectives are met effectively and efficiently. |
| Infrastructure Testing | The infrastructure will be able to handle high traffic and large datasets without significant performance degradation. | Scalability is key to ensuring the system performs well under various conditions. |
| Privacy and Security | Security measures (encryption, access control) will be implemented and adhered to by all stakeholders. | Protecting the integrity and privacy of sensitive data ensures trust and legal compliance. |
| User Training and Support | Adequate training and support will be provided to users for smooth adoption and system usage. | User training is essential for effective implementation and maximizing system adoption. |
| Open Source Compliance | The open-source project will comply with all relevant licensing and contribution guidelines. | Open-source compliance is essential for legal and community engagement, as well as for transparency. |
| Deployment and Maintenance | Deployment will occur on schedule, with support and maintenance provisions for system upgrades and bug fixes. | Continuous support is necessary for addressing issues and ensuring long-term project sustainability. |
| UI/UX Design | The user interface will be intuitive and user-friendly, meeting the expectations of the target user groups. | A well-designed interface improves user adoption and minimizes errors or inefficiencies. |
8. Project Constraints
| Constraint Area | Constraint Description | Impact/Reasoning |
|---|---|---|
| Time | Limited project timeline due to funding cycles, stakeholder expectations, or other external deadlines. | Rushed development phases may lead to incomplete features, missed testing, or inadequate documentation. |
| Budget | Fixed or limited project budget that may restrict resource allocation, including tools, cloud infrastructure, and personnel. | Budget constraints could limit the scale of deployment, the number of users involved in testing, or the amount of refinement possible for the system. |
| Technology Stack | Dependencies on specific software, frameworks, or platforms (e.g., AI models, NLP tools) that may limit flexibility or add complexity. | Limited flexibility in choosing technologies could hinder the ability to adapt the system to new requirements or improvements. |
| Data Availability | Data sources may be incomplete, inaccessible, or difficult to integrate due to privacy, ethical, or legal constraints. | Lack of access to diverse, high-quality data may compromise the accuracy and generalization of AI models. |
| Privacy and Compliance | Stringent privacy regulations (e.g., GDPR, UNICEF standards) may limit how data can be processed, stored, and shared. | Privacy laws will affect how data is collected, used, and shared, requiring extensive compliance measures and potentially limiting the scope of data used in the project. |
| User Adoption and Engagement | Difficulty in securing active engagement from target users (e.g., child helplines) for testing or deployment. | Low user engagement may result in insufficient testing or slower adoption of the final product, limiting its effectiveness. |
| Team Skills and Capacity | Constraints related to available team expertise and capacity, especially in specialized areas like AI ethics, voice recognition, and NLP. | Lack of skilled personnel could delay development, impact the quality of the system, or lead to suboptimal decisions in design and implementation. |
| Infrastructure | Limited access to required computing resources (e.g., cloud storage, GPU for training models) or bottlenecks in data handling capacity. | Infrastructure constraints could hinder the ability to scale the system, limit model training capabilities, or introduce performance bottlenecks. |
| Integration with Existing Systems | Dependencies on integrating the AI system with existing systems (e.g., child helplines’ call management tools), which may require custom development or adjustments. | Integration issues could delay deployment, complicate user workflows, or create technical debt if not carefully managed. |
| Security Risks | Potential security vulnerabilities or limitations in implementing robust data protection mechanisms. | Insufficient security measures can lead to data breaches, legal non-compliance, and loss of stakeholder trust. |
| Ethical Considerations | Ethical constraints in using AI for sensitive topics such as VAC/GBV, and ensuring the system does not inadvertently cause harm. | Ethical concerns may require additional review processes, slow down decision-making, or necessitate system redesigns to ensure fair and responsible use of the technology. |
| External Dependencies | Reliance on third-party vendors, cloud services, or external organizations (e.g., UNICEF compliance checks). | Dependencies on external parties can introduce delays or risks if they do not meet expected timelines or standards. |
| Deployment Constraints | Limited ability to deploy the system due to geographic, regulatory, or technological factors (e.g., internet connectivity, mobile network availability in target regions). | Deployment may be limited to certain areas or populations, affecting the reach of the project and its scalability. |
| Sustainability and Maintenance | Long-term project sustainability is constrained by ongoing maintenance, support costs, and resource allocation post-deployment. | Failure to allocate sufficient resources for ongoing support can lead to degraded system performance and loss of user confidence. |
| Scalability | Difficulty in scaling the system due to technology or infrastructure limitations. | The system may struggle to handle increased data volume, user load, or additional feature requirements over time. |
9. Risks and Mitigation Strategies
| Risk Area | Risk Description | Mitigation Strategy |
|---|---|---|
| Delays in Deliverables | Delays in project milestones or deliverables due to unexpected technical challenges, resource shortages, or stakeholder involvement. | - Implement a detailed project timeline with buffer periods for each milestone. - Hold regular progress reviews and adapt the plan as necessary. |
| Technical Challenges | Unforeseen technical difficulties, such as AI model performance issues, data integration challenges, or infrastructure limitations. | - Conduct early prototype testing to identify technical barriers. - Engage subject matter experts to resolve complex issues. |
| User Adoption Issues | Difficulty in gaining adoption from child helplines or target users due to lack of understanding or resistance to change. | - Provide thorough training and demonstrations of the system’s benefits. - Gather user feedback early and iterate on the design based on this input. |
| Data Privacy & Compliance Risks | Risks related to non-compliance with data protection laws, such as GDPR, or privacy violations in handling sensitive data (e.g., voice recordings, PII). | - Work closely with legal and compliance teams to ensure all data collection, processing, and storage activities are compliant. - Conduct regular privacy audits. |
| AI Model Accuracy and Bias | The AI models may not perform as expected, or they could introduce bias, affecting case predictions or intervention recommendations. | - Regularly evaluate model performance using real-world data and performance benchmarks (e.g., precision, recall). - Implement fairness checks and continuous model retraining to address bias. |
| Infrastructure Scalability Issues | Difficulty in scaling the system to handle increased data loads, user numbers, or transaction volumes. | - Conduct stress testing and scalability tests during the development phase. - Design the system with scalability in mind, using cloud-based solutions. |
| Integration Challenges | Difficulty integrating the AI solution with existing child helpline systems or third-party services. | - Engage with existing system owners early to identify integration points and challenges. - Use standardized APIs and open protocols for ease of integration. |
| Security Vulnerabilities | Security threats, such as data breaches or cyberattacks, could jeopardize project data and user trust. | - Implement robust encryption, secure access protocols, and regular security audits. - Use penetration testing to identify vulnerabilities before deployment. |
| Stakeholder Alignment | Misalignment between stakeholders, leading to differing expectations, priorities, or resource allocation. | - Establish clear communication channels and regular stakeholder meetings to align expectations. - Set clear project goals and priorities early in the process. |
| Team Skill Gaps | Lack of required skills within the project team, particularly in areas such as AI, NLP, or child protection. | - Invest in training for team members. - Hire or consult with experts in AI ethics, voice recognition, and child protection as needed. |
| Ethical Concerns in AI Deployment | Potential ethical dilemmas related to the use of AI in sensitive contexts like VAC/GBV cases. | - Conduct ethical reviews regularly and follow industry guidelines. - Collaborate with child protection experts and UNICEF advisors. |
| Budget Overruns | The project may exceed its allocated budget due to unforeseen costs or scope changes. | - Establish a clear budget plan with allocated contingencies for unexpected costs. - Monitor expenses regularly and adjust the scope as necessary. |
| Dependency on Third-Party Vendors | Delays or failures from third-party service providers (e.g., cloud infrastructure, data providers) could affect project timelines. | - Negotiate clear service-level agreements (SLAs) with vendors. - Have contingency plans in place, including backup vendors or manual processes. |
| Data Quality and Availability | Insufficient or low-quality data could impact model performance or delay development. | - Ensure clear agreements with data providers and implement robust data quality assurance processes. - Explore alternative data sources if primary data becomes unavailable. |
| User Interface/Experience (UI/UX) Issues | User experience problems that hinder effective usage, leading to lower adoption or user dissatisfaction. | - Conduct early user testing and iterate on design based on feedback. - Prioritize user-friendly UI/UX design principles and simplicity. |
| Regulatory Changes | Changes in regulations or laws (e.g., data privacy laws, child protection regulations) that could impact the project. | - Regularly monitor relevant legal frameworks and adapt project practices as required. - Work closely with legal advisors to stay updated. |
| Model Deployment Delays | Delays in the deployment phase due to technical, ethical, or regulatory hurdles. | - Set realistic deployment milestones and monitor progress closely. - Conduct rigorous pre-deployment testing to ensure readiness. |
| Sustainability and Maintenance | Lack of resources or focus on long-term maintenance could lead to system degradation over time. | - Plan for ongoing support and maintenance post-deployment. - Allocate a dedicated team or budget for long-term updates and system health checks. |
10. Roles and Responsibilities
| Role | Responsibilities |
|---|---|
| Project Manager | - Oversee overall project progress and ensure alignment with objectives and timelines. - Manage team coordination and communication. - Track milestones, risks, and issues, and ensure they are addressed. - Communicate with stakeholders and manage expectations. |
| System Architect | |
| AI/ML Engineer | - Develop and train AI models (e.g., NLP, voice recognition) for case prediction and identification. - Fine-tune models based on feedback and real-world data. - Integrate AI models into the broader system. - Monitor and improve model performance. |
| Software Developer | - Develop the software components (backend and frontend) for the AI system, ensuring integration with existing systems. - Implement system architecture, APIs, and user interfaces. - Write clean, maintainable, and scalable code. |
| Data Scientist | - Work on data preprocessing, data exploration, and data quality assurance. - Label datasets and develop prediction models. - Analyse data to identify insights for AI model training. - Work with AI engineers to refine models. |
| Quality Assurance (QA) Engineer | - Develop and execute test plans to ensure the system meets functional, security, and performance standards. - Identify bugs and issues, ensuring they are resolved before deployment. - Conduct regression, load, and security testing. |
| UX/UI Designer | - Design user-friendly and intuitive interfaces for the system, focusing on accessibility and usability. - Conduct user testing and incorporate feedback into design iterations. - Work closely with developers to ensure the implementation of UI/UX designs. |
| Child Protection | - Provide guidance on ethical considerations and child protection laws. - Ensure the system adheres to child welfare standards. - Advise on the interpretation of sensitive data and ensure ethical data handling practices. |
| Legal | - Ensure compliance with data privacy regulations (e.g., GDPR, UNICEF standards). - Review contracts, data-sharing agreements, and the project’s legal documentation. - Advise on intellectual property, licensing, and ethical considerations. |
| Stakeholders (Child Helpline Managers, End-Users, Government, Partners) | - Provide input on system requirements and expected outcomes. - Offer feedback during user testing phases. - Assist in the deployment of the solution within existing child helpline systems. - Ensure that the solution meets the needs of the target users. |
| System Administrator | - Manage cloud infrastructure, deployment, and scaling. - Ensure the system is secure, with proper access control and data encryption. - Monitor system performance and address any issues related to server downtime or capacity. |
| Data Privacy and Security Officer | - Ensure compliance with data privacy laws and security standards. - Implement encryption and security protocols for sensitive data (e.g., voice recordings, personally identifiable information). - Conduct regular security audits and vulnerability assessments. |
| Content/Communications Specialist(Non Project) | - Develop content for blog posts, social media, and project documentation. - Ensure consistent messaging across all communications channels. - Engage with stakeholders and the public to raise awareness about the project. |
| Sponsor/Executive Stakeholders(UNICEF) | - Provide funding and high-level direction for the project. - Ensure that the project aligns with organisational goals and priorities. - Support the project team by removing blockers and securing additional resources when necessary. |
| Mentors/Advisors(UNICEF) | - Provide guidance on open-source compliance, contributions, and community building. - Ensure the project adheres to open-source principles and best practices. - Help with the development and management of the project's open-source community. |
11. Governance and Reporting Structure
The governance and reporting structure for the project will ensure that the project is properly managed, progress is tracked, and all stakeholders remain informed throughout the project's lifecycle. Below is a suggested structure:
Project Management Oversight
The project will be overseen by a Project Steering Committee (or a leadership team) composed of senior stakeholders, project managers, and key subject matter experts. This committee will be responsible for the overall direction of the project, making key decisions, approving changes, and addressing critical issues as they arise.
Roles in Steering Committee:
- Project Manager (Chairperson)
- Senior Stakeholders (e.g., representatives from the funding agency, child helpline managers)
- Legal Advisor
- AI/ML Lead
- Data Privacy Officer
- Child Protection Expert
Reporting Cadence
- Weekly Status Updates: A brief meeting with the core project team to review ongoing tasks, milestones, and issues. The Project Manager will provide a summary of the project's current status, highlighting achievements and identifying any risks.
- Bi-weekly Steering Committee Meetings: These meetings will involve a deeper review of project milestones, budget updates, resource allocation, and risk management. The Project Manager will present a detailed status report, including progress against KPIs, identified risks, and any actions needed from the steering committee. These meetings also allow stakeholders to provide feedback on the direction and strategic adjustments.
- Monthly Reports: A comprehensive report summarising progress, completed tasks, any deviations from the plan, challenges, and risks encountered. This will be shared with all stakeholders, including external partners, and will contain both qualitative and quantitative metrics.
- Quarterly Review and Feedback Sessions: These sessions will allow the project team to gather feedback from external stakeholders, including child helplines, end users, and advisors. The focus will be on lessons learned, adjustments needed in the approach, and user feedback on the functionality and effectiveness of the AI systems.
Communication Channels
- Project Management Tools: The project team will utilize collaboration and project management tools such as Trello, Jira, or Asana to manage tasks, track progress, and assign responsibilities. These tools will help ensure visibility for all team members and allow easy tracking of milestones, deadlines, and issues.
- Email and Direct Messaging:
- Email will be used for formal communication, updates, and sharing of official documents or reports.
- Slack or similar messaging platforms will be used for day-to-day communication, quick queries, and informal updates. Dedicated channels can be set up for different areas of the project (e.g., AI team, legal/ethical considerations, UI/UX feedback).
- Document Sharing: The team will use cloud-based platforms like Google Drive or Dropbox to store and share documentation, including meeting minutes, technical documentation, design files, and project reports. This ensures that all team members and stakeholders have access to the latest documents.
- Version Control and Collaboration: The development team will use GitHub or GitLab for version control, enabling collaborative code development and ensuring that all changes are tracked and documented.
Risk Management and Issue Resolution
- Risk Review Sessions: Risks will be monitored continuously, with a dedicated session during the bi-weekly steering committee meetings to review existing and new risks. The committee will identify mitigation strategies for identified risks and approve necessary adjustments to the project plan.
- Issue Escalation Path: If issues arise that cannot be resolved within the team, they will be escalated to the project manager. If further intervention is required, the project manager will raise the issue with the steering committee. For urgent matters, a dedicated meeting will be called to resolve issues as quickly as possible.
Key Deliverables and Milestone Reviews
- Milestone Reports: The completion of key milestones will be followed by detailed reporting and reviews with stakeholders. These reviews will focus on:
- Deliverables completion
- Impact on overall project timeline
- Lessons learned and feedback for future phases
- Adjustments or additional support needed
Decision-Making Process
- Consensus-Based Decision Making: The project will aim for decisions to be made through consensus within the project team and stakeholders. In cases where consensus cannot be reached, the Project Manager, supported by the Steering Committee, will make the final decision.
12. Approval and Sign-Off
The following stakeholders will be responsible for reviewing, approving, and signing off on the project scope document to ensure alignment with organizational goals, project objectives, and regulatory standards:
1. Project Steering Committee
- Project Manager (Chairperson): Responsible for ensuring the project scope aligns with the overall vision and objectives. The project manager will finalize the scope document and present it to the committee for approval.
- Senior Stakeholder(s) (e.g., representatives from UNICEF or funding agency): Senior leadership from the funding organization or project sponsor will review the scope to ensure it meets funding requirements and strategic objectives.
- Legal Advisor: Responsible for reviewing compliance with legal requirements, ensuring that data protection laws (e.g., GDPR) and contractual obligations are met.
- AI/ML Lead: Reviews the technical feasibility and alignment with AI/ML project goals, ensuring the scope supports technical execution and addresses potential risks.
- Data Privacy Officer: Reviews the document to ensure all privacy and data protection measures, such as anonymization and secure storage, are clearly defined and adhere to local and international laws.
- Child Protection Expert: Ensures the project scope adequately addresses child protection concerns, especially in handling sensitive data related to VAC/GBV cases.
2. Project Team Leads
- AI/ML Team Lead: Reviews the technical sections of the project scope, particularly the AI model performance metrics and feasibility.
- UI/UX Team Lead: Ensures the user interface design and experience described in the scope aligns with project goals for user adoption and accessibility.
- Data Engineering Lead: Confirms that the project scope appropriately covers data infrastructure, collection, and processing needs.
3. Stakeholders from Partner Organizations
- Child Helpline Representative(s): Reviews the scope to ensure the project’s objectives are in line with the operational requirements and needs of child helplines.
- External Privacy and Security Consultant (if any): Provides input on the privacy strategies outlined in the document, ensuring they align with best practices and regulations.
4. Executive Leadership
- Chief Executive Officer (CEO) or Equivalent: The final signatory from the project's executive leadership to formally approve and endorse the document for execution. The CEO will ensure that the project’s scope aligns with the strategic direction and financial resources available.
5. Legal and Compliance Officer
- Compliance Officer: Responsible for confirming that the project scope complies with both internal and external regulatory requirements, including child protection laws, privacy policies, and open-source compliance.