# RBM Logic Model Sample: Responsible AI Capacity for Civil Service (Kazakhstan, 2026-2027)

## Theory of Change

If national and subnational civil servants in Kazakhstan receive practical training, coaching, and implementation support on responsible AI design, governance, and service delivery, and if ministries adopt minimum governance procedures for AI use, then public institutions will improve their ability to deploy safe, accountable, and citizen-centered AI-enabled services. This will contribute to stronger public-sector performance and trust over a 3-5 year horizon.

Causal pathway:

`Inputs -> Activities -> Outputs -> Outcomes (skills + institutional adoption + service quality) -> Impact (institutional effectiveness and citizen trust)`

Key assumptions:
- Ministries continue to authorize staff time for training and pilot work.
- Basic digital infrastructure and data access remain available in participating agencies.
- AI policy/regulatory guidance remains stable enough to operationalize during 2026-2027.

## Executive Summary

This sample RBM model describes a 24-month nonprofit/implementer program to strengthen responsible AI competencies in Kazakhstan's civil service, with a 3-5 year institutional impact horizon. The model links training and technical assistance to measurable behavior and system changes, including adoption of governance procedures and improved quality of AI-enabled services. Indicators are SMART and aligned to practical donor reporting requirements, with explicit assumptions where baseline data must be collected at project start.

## Logic Model Table (Inputs to Impact)

| Level | Statements |
| --- | --- |
| Inputs | Donor funding (assume USD 2.2M); technical team (AI governance lead, M&E lead, 4 trainers, local facilitators); curriculum and learning platform; partnerships with civil service academy and 8 pilot ministries/agencies; monitoring systems and independent data quality support. |
| Activities | Competency baseline assessment; delivery of modular training (responsible AI, risk management, procurement, ethics-by-design); coaching for ministry implementation teams; development of agency AI governance SOPs/checklists; implementation of ministry pilot projects; periodic peer-learning and policy-dialogue sessions. |
| Outputs | 1) Civil servants trained and certified by role track. 2) Agency teams coached with documented implementation plans. 3) Drafted and approved AI governance SOPs/checklists. 4) Pilot use-cases launched with monitoring dashboards. 5) Review workshops and lessons-learned products completed. |
| Outcomes (short/medium term) | O1 (by month 24): Civil servants in participating agencies demonstrate improved practical responsible AI skills. O2 (by month 24): Participating agencies institutionalize minimum AI governance controls in planning/procurement/implementation processes. O3 (by month 24): Pilot public services apply responsible AI practices and show measurable quality improvements. |
| Impact (3-5 years) | Public institutions in participating sectors deliver more effective, transparent, and accountable digital services, contributing to increased citizen trust and stronger public-sector performance. |

## Outcome Indicators (grouped by outcome)

### Outcome 1: Civil servants improve practical responsible AI competencies by month 24

| Indicator | Definition / Formula | Baseline | Target | Frequency | Data source | Disaggregation |
| --- | --- | --- | --- | --- | --- | --- |
| O1.1 Share of trained participants scoring at or above proficiency threshold | `(# participants with post-test score >= 70%) / (total tested participants) * 100` | 22% (assumption; to verify in month 1 baseline) | >= 75% by month 24 | Quarterly | LMS tests + practical assessments | Sex, age band, ministry, region, role level |
| O1.2 Average practical assessment score improvement | `Average post-test score - average pre-test score` (percentage points) | 0 pp at baseline | >= +25 pp by month 24 | Quarterly cohort closeout | Pre/post assessments | Sex, ministry, role level |
| O1.3 Share of trained staff applying at least one responsible AI tool in active workflows | `(# trained staff reporting verified use in prior 90 days) / (total trained staff sampled) * 100` | 10% (assumption; verify in month 3) | >= 60% by month 24 | Semi-annual | Supervisor verification + workflow artifacts | Sex, ministry, region |
| O1.4 Training completion rate among enrolled participants | `(# participants completing full track) / (total enrolled participants) * 100` | 0% at project start | >= 85% by month 24 | Monthly | LMS completion logs | Sex, age band, disability status (where consented) |

### Outcome 2: Participating agencies institutionalize AI governance controls by month 24

| Indicator | Definition / Formula | Baseline | Target | Frequency | Data source | Disaggregation |
| --- | --- | --- | --- | --- | --- | --- |
| O2.1 Share of participating agencies with formally approved AI governance SOP/checklist package | `(# agencies with approved package) / (total participating agencies) * 100` | 0% | >= 80% by month 18; 100% by month 24 | Quarterly | Agency orders/approval records | Agency type (ministry/agency), sector |
| O2.2 Share of new AI-related initiatives that pass governance review before implementation | `(# AI initiatives passing documented governance review pre-launch) / (total new AI initiatives reviewed) * 100` | 15% (assumption; verify in month 2 mapping) | >= 85% by month 24 | Quarterly | Governance review logs | Agency, initiative type |
| O2.3 Median turnaround time for governance review of AI initiatives | Median calendar days from submission to decision | 45 days (assumption; verify in month 2) | <= 20 days by month 24 | Quarterly | Agency workflow records | Agency |
| O2.4 Share of agencies publishing internal accountability roles for AI oversight | `(# agencies with published role matrix for AI oversight) / (total participating agencies) * 100` | 5% (assumption) | >= 90% by month 24 | Semi-annual | Policy documents, HR notices | Agency, sector |

### Outcome 3: Pilot services apply responsible AI practices and improve service quality by month 24

| Indicator | Definition / Formula | Baseline | Target | Frequency | Data source | Disaggregation |
| --- | --- | --- | --- | --- | --- | --- |
| O3.1 Share of pilot services meeting minimum responsible AI compliance checklist at review | `(# pilot services meeting all mandatory checklist items) / (total pilot services reviewed) * 100` | 0% | >= 80% by month 24 | Quarterly | Compliance checklist audits | Service type, agency |
| O3.2 Average citizen satisfaction score for pilot services | Mean score on standardized user feedback instrument (1-5 scale) | 3.1 (assumption; verify in baseline user survey) | >= 4.0 by month 24 | Semi-annual | Citizen surveys + service feedback channels | Sex, age band, location, vulnerable group marker (if available) |
| O3.3 Percentage reduction in service processing time for pilot workflows | `((baseline days - current days) / baseline days) * 100` | 0% | >= 30% reduction by month 24 | Quarterly | Administrative service records | Service type, region |
| O3.4 Share of pilot services with documented incident response and model monitoring logs | `(# pilot services with complete logs in prior quarter) / (total pilot services) * 100` | 10% (assumption) | >= 85% by month 24 | Quarterly | Incident logs, monitoring dashboards | Service type, agency |

## SDG Alignment

| Results level | SDG goal/target | Rationale |
| --- | --- | --- |
| Outcome 1 | SDG 4.4 | Expands relevant technical and digital skills among public servants. |
| Outcome 2 | SDG 16.6 | Strengthens effective, accountable, and transparent institutions through AI governance controls. |
| Outcome 3 | SDG 16.6 | Improves quality and accountability of public services using monitored responsible AI practice. |
| Outcome 1/2 (inclusion dimension) | SDG 5.b | Promotes enabling use of digital technology and inclusion in public-sector capacity pathways. |
| Impact | SDG 16.6 and SDG 9.c (indirect) | Supports institutional performance and resilient digital public infrastructure practices. |

## Data Collection Plan

| Component | Method | Frequency | Owner |
| --- | --- | --- | --- |
| Baseline study | Competency assessment, institutional governance mapping, and service baseline extraction | Month 1-2 (once) | M&E Lead + External QA advisor |
| Routine training monitoring | LMS analytics, attendance, completion, pre/post tests | Monthly and quarterly aggregation | Training Manager |
| Governance adoption tracking | Document review + structured agency verification checklist | Quarterly | Governance Specialist + Agency focal points |
| Service quality tracking | Administrative data pulls + citizen feedback review | Quarterly/Semi-annual | Service Delivery Lead + M&E Officer |
| Midline review | Outcome progress review and target recalibration | Month 12 | Program Director + M&E Lead |
| Endline evaluation | Repeat baseline instruments + comparative analysis | Month 24 | External evaluator + M&E Lead |
| Follow-up windows | Sustainability check on outcome persistence | 6 and 12 months post-close | Program team with agency focal points |
| Data quality assurance | Indicator reference sheets, spot checks, duplicate checks, missing-data logs, quarterly DQA meeting | Quarterly | M&E Lead (accountable), Data Steward (operational) |

## Assumptions and Risks

Assumptions used due to incomplete input:
- Budget, participating agency count, and several baselines are assumed and must be replaced after inception baseline.
- Donor reporting cadence is assumed to be quarterly with annual performance summary.

Key risks and mitigations:
- Staff turnover in agencies could reduce retention of trained competencies.
  - Mitigation: institutionalize training-of-trainers and e-learning refreshers.
- Policy shifts or approval delays could slow SOP adoption.
  - Mitigation: phased approval pathways and early legal/policy engagement.
- Uneven regional digital infrastructure may limit pilot quality gains.
  - Mitigation: prioritize adaptable pilot designs and infrastructure-sensitive rollout.
- Baseline data gaps may weaken early attribution confidence.
  - Mitigation: front-load baseline protocol and data quality checks in months 1-2.
