Centram Centre for Applied Artificial Intelligence

AI ANALYTICS AND AUTOMATION COURSE FOR GENDER, WOMEN & SOCIAL AFFAIRS MANAGEMENT

Course overview

This course is designed as a practical, policy‑aware course for Gender, Women & Social Welfare Management.” It follows the format used in the sector courses above and is designed for policy makers, program managers (social protection, gender, GBV, livelihoods), M&E teams, caseworkers, frontline social welfare staff, hotlines/helpline operators, community organisations, statisticians, and data/IT teams that support them.
Target audience

Gender equality officers, social protection managers, child/woman protection caseworkers, GBV referral coordinators, M&E specialists, social statisticians, frontline supervisors, helpline managers, community organisers, data scientists/engineers supporting welfare programs.

Course learning outcomes

By course end participants will be able to:

  1. Design privacy‑preserving, auditable data pipelines linking administrative registries, household surveys, service delivery logs, helpline records, market/labour data and geospatial layers for gender‑responsive decision making.
  2. Produce gender‑disaggregated analytics and intersectional indicators (age, disability, ethnicity, location) and use them to prioritise services, design targeting rules and measure program equity and impact.
  3. Develop safe automation for routine workflows (eligibility screening, referral routing, appointment scheduling, case triage, SMS/IVR) with centred human‑in‑the‑loop safeguards for sensitive cases (GBV, child protection).
  4. Apply causal and quasi‑experimental methods to evaluate programs, forecast caseloads, and optimise resource allocation (caseworker assignments, shelter capacity).
  5. Operationalise governance and safeguarding: informed consent, data minimisation, child protection, survivor confidentiality, FPIC for vulnerable groups, privacy by design, bias/fairness audits and grievance/redress procedures.

Introduction: policy goals, stakeholders & use cases

  1. Objectives: Connect national gender and social welfare objectives (poverty reduction, GBV response, childcare, livelihoods, inclusive services) to analytics
  2. se cases and stakeholders.
  3. Topics: Program types (cash transfers, shelters, childcare, livelihoods), referral pathways, ecosystem mapping (NGOs, health, police, social registries), high‑value use cases and constraints.
  4. Lab: Problem scoping — convert a priority (e.g., reduce time‑to‑shelter placement for GBV survivors) into metrics, data needs, actors and evaluation design.

Data governance, consent, safeguarding & legal frameworks

  1. Objectives: Establish governance suitable for highly sensitive personal data and vulnerable groups.
  2. Topics: Data protection law, consent and assent, child protection, GBV confidentiality and survivor safety, data minimisation, secure enclaves, role‑based access, anonymization, retention rules, FPIC, data sharing agreements, grievance/appeals.
  3. Lab: Draft a data classification/ access matrix and a survivor‑safe consent & referral metadata schema for program records.

 Ingestion & standardisation: registries, surveys, helplines & IoT

  1.  Objectives: Ingest and standardise heterogeneous sources: social registries, case management systems, DHS/MICS, helpline logs, SMS/IVR, mobile money payments, market/labour feeds, geospatial poverty layers.
  2. Topics: Schemas and unique IDs, entity resolution, deduplication, timestamp alignment, low‑bandwidth data collection (USSD/IVR), metadata for sensitivity flags.
  3. Tools: ODK/KoBo, RapidPro/FrontlineSMS, Postgres/PostGIS, Python/R ETL, data quality frameworks.
  4. Lab: Build an ETL that ingests registry records + helpline transcripts + payment disbursements into a canonical, access‑controlled dataset with provenance.

Gender‑disaggregated indicators & intersectional analysi

  1. Objectives: Produce disaggregated indicators and intersectional analyses for monitoring and targeting.
  2. Topics: Indicator construction (gender gaps, unpaid care time), intersectionality (age, disability, ethnicity), small‑area estimation, sample weighting, design effects, confidence intervals for disaggregated groups.
  3. Tools: R survey package, small‑area estimation tools, GeoPandas/QGIS.
  4. Lab: Generate disaggregated poverty/food security and service access maps; produce uncertainty estimates and data‑quality notes.

Targeting, eligibility & equitable automation

  1.  Objectives: Design targeting algorithms that prioritise equity and minimise exclusion and harms.
  2. Topics: Proxy means tests vs categorical targeting, participatory targeting, geographic targeting, threshold effects, gaming risks, targeting fairness and transparency, appeals mechanisms.
  3. Tools: Logistic/ML classifiers with fairness constraints, record linkage, decision records and model cards.
  4. Lab: Build a targeting prototype using household proxies and geospatial indicators; run inclusion/exclusion and fairness analyses and design an appeals workflow.

 Case management automation, triage & referral systems

  1. Objectives: Automate safe triage and referral routing for sensitive caseloads while preserving confidentiality and human oversight.
  2. Topics: Caseworker workflows, triage scoring (urgency/risk), referral networks (health, legal, shelter), survivor consent & disclosure minimisation, escalation rules, audit trails.
  3.  Tools: Workflow engines, case management systems (open source examples), secure messaging patterns.
  4. Lab: Prototype a triage+referral workflow: helpline intake → risk scoring → recommended referrals → human approval and logging.

NLP for helplines, complaints & social listening

  1. Objectives: Use text/audio analytics to extract signals from helplines, case notes, social media and community feedback while managing privacy.
  2. Topics: ASR for call transcripts, topic modelling, sentiment and urgency detection, anonymisation and metadata stripping, false‑positive risks, responsible social listening for GBV.
  3. Tools: Whisper/ASR, spaCy/Hugging Face transformers, topic models, safe‑display patterns.
  4. Lab: Build a pipeline to transcribe sampled helpline calls, extract urgency/issue tags and route high‑risk cases to analysts with redaction and evidence logs

Predictive analytics for caseload forecasting & resource optimisation

  • Objectives: Forecast demand (shelter beds, cash uptake, hotline calls) and optimise staff scheduling and resource allocation.
  • Topics: Time‑series forecasting, seasonality and shocks (economic, pandemic), scenario planning, capacity planning and optimisation, fairness constraints in allocation.
  • Tools: Prophet/ARIMA/LSTM, OR‑Tools for allocation, dashboards for scenarios.
  • Lab: Build a forecast for shelter demand and an optimisation demo that assigns caseworkers and beds under constraints, with fairness considerations.

Impact evaluation & causal analytics

  1.  Objectives: Apply rigorous evaluation methods to measure program effects and equity impacts.
  2. Topics: RCT basics, quasi‑experimental methods (RDD, DID, propensity scores, synthetic controls), pre‑analysis plans, multiple hypothesis testing, subgroup treatment effects, ethical considerations.
  3. Tools: R/ Stata/ Python causal inference libraries, matching packages, power calculations.
  4. Lab: Design and analyse a quasi‑experimental evaluation (DID or matching) for a cash‑plus program, with subgroup impact estimates and limitations documented.

Financial flows, fraud detection & conditionality monitoring

  1. Objectives: Monitor digital payments, detect diversion/fraud and track conditionality compliance without exposing beneficiaries.
  2. Topics: Payment reconciliation, anomaly detection on disbursements, leakage estimation, conditional cash transfer compliance monitoring, privacy preserving audits.
  3. Tools: Transaction analytics, statistical reconciliation, anomaly detectors, privacy preserving aggregation.
  4. Lab: Reconcile sample disbursement logs to registry receipts, flag suspicious patterns and produce secure audit reports.

Safeguards, explainability, fairness & governance

  1. Objectives: Audit models and automation for fairness, safety and rights protection; create governance instruments and SOPs.
  2. Topics: Algorithmic impact assessments, model cards, explainability (SHAP/local explanations), monitoring for drift and unintended harms, grievance/redress, survivor safety protocols, procurement safeguards.
  3. Tools/Patterns: SHAP, fairness toolkits (fairlearn), model registries, monitoring dashboards.
  4.  Lab: Run a fairness and safety audit on a targeting/triage model, produce a model card and operational SOP limiting uses and specifying human review.

Orchestration, M&E, stakeholder engagement & capstone

  1. Objectives: Deploy production pipelines, monitoring and present capstone projects with operational and safeguarding documentation.
  2. Topics: Orchestration (Airflow/Prefect), MLOps (versioning/CI), monitoring (data/model drift), KPIs and dashboards, community and civil society engagement strategies, communications and consent for public dashboards.
  3.  Capstone: Teams deliver a reproducible prototype (e.g., gender‑disaggregated dashboard + targeting prototype; safe helpline ASR + triage workflow; shelter demand forecast + allocation optimizer) plus a policy/operational brief, safeguarding plan and demo.

Capstone structure

  1. Problem selection, stakeholder mapping, data assembly & baselines
    – Week 2: Pipeline & prototype implementation (ingest → model/automation → UI)
  2. Evaluation, safeguarding/consent statement, SOPs and presentation
    – Deliverables: reproducible repo + Dockerfile, provenance logs, evaluation report, model card, safeguarding plan and policy brief.

Operational KPIs & evaluation metrics

  1. Programmatic: time‑to‑referral, time‑to‑shelter placement, service uptake rates (disaggregated), reduction in unmet need.
  2. Predictive systems: precision/recall for urgent case detection, MAE for demand forecasting, calibration and fairness metrics across subgroups.
  3. Governance: proportion of automated decisions human‑reviewed, grievance rates & resolution times, consent coverage, access log completeness.
  4. Evaluation: estimated treatment effects and confidence intervals, subgroup impacts and robustness checks.

Recommended tools, libraries & datasets

  1. Languages/infra: Python, R, SQL, Docker, Airflow/Prefect, Postgres/PostGIS
  2. Data collection: ODK/KoBo, RapidPro/FrontlineSMS, IVR/USSD platforms, mobile money APIs (for payment labs)
  3. NLP & ASR: Whisper/other ASR (careful with privacy), spaCy, Hugging Face transformers, sentence‑transformers
  4. Analytics & ML: scikit‑learn, XGBoost/LightGBM, Prophet, causal inference libs (DoWhy, CausalImpact), SHAP/fairlearn
  5. GIS & small‑area estimation: QGIS, GeoPandas, R survey, SAE packages
  6. Case management & workflow: open‑source CMS/CMIS examples, workflow engines
  7. MLOps & monitoring: MLflow, Evidently/WhyLabs, Grafana
  8. Public datasets & sources: DHS, MICS, UN Women databases, World Bank gender indicators, national social registries (where available), labour force surveys
  9. Synthetic data: SDV, Faker, synthetic audio/text for helpline labs to avoid    exposing real PII or survivor data

          Key risks, safeguards & mitigation

  1. Privacy & survivor safety: never expose identifying details for GBV/child protection; use secure enclaves, strict role‑based access, minimised data retention and safe redaction; aggregate public outputs.
  2.  Re‑traumatisation & consent: design data collection and automation to avoid retraumatising survivors; obtain informed consent and allow withdrawal; use anonymised, trauma‑informed intake scripts.
  3.  Bias & exclusion: monitor models for disparate impacts (gender, age, disability, minority groups); prioritise inclusive sampling and participatory validation.
  4. Over‑automation risks: human‑in‑the‑loop for sensitive decisions (referrals, eligibility rejections); maintain appeal/redress channels.
  5. Misuse & political risks: governance over who accesses sensitive analytics; multi‑stakeholder oversight and transparency about limitations.
  6.  Child protection & legal obligations: ensure mandatory reporting protocols are adhered to; implement safe handling workflows.
  7. Vendor & procurement risk: require reproducibility, data‑use limits, survivor‑safety clauses, indemnities and training/operational support in contracts.
  8.  Quality & coverage gaps: invest in representative data, community verification and complementary qualitative methods.

Practical lab/project ideas

  1. Gender‑disaggregated access dashboard with small‑area estimates and uncertainty notes.
  2. Targeting prototype for a cash‑plus program with fairness audits and appeals workflow.
  3. Safe helpline pipeline: ASR (synthetic), urgency detection, triage and referral queue with redaction and human review.
  4. Shelter demand forecasting + bed/caseworker allocation optimiser with fairness constraints.

 Text analysis of case notes/complaints to surface themes, service gaps and emergent