Introduction

Government analysts preparing AI investment cases lack tools that model AI-specific risks alongside standard public sector procurement risks. Existing ROI calculators — both manual and automated — treat AI projects identically to conventional IT deployments, ignoring risks unique to machine learning systems: data drift requiring retraining, algorithmic bias with documented legal and political consequences, model performance degradation, and specialized talent competition with the private sector.

We contribute an open-source Monte Carlo simulation tool that enables government analysts to stress-test AI investment cases against nine risk factors. Four are standard government project risks calibrated from public administration literature. Five are AI-specific risks calibrated from documented real-world incidents and ML engineering literature. The tool accepts user-specified investment parameters and outputs probability distributions of NPV, IRR, and BCR, enabling analysts to explore scenario ranges rather than relying on single-point deterministic estimates.

We demonstrate the tool on two example configurations — tax administration in Brazil and municipal services in Saudi Arabia — to illustrate its operation. These are example inputs demonstrating tool functionality, not empirical evaluations of actual projects.

Risk Taxonomy with Empirical Calibration

Standard Government Project Risks

AI-Specific Risks (Calibrated from Documented Incidents)

Design note: The algorithmic bias distribution was calibrated against three documented government algorithmic failures with known costs (Dutch childcare, Australia Robodebt, Michigan MiDAS), not estimated from first principles. As the database of government AI incidents grows, these distributions should be updated.

Methodology

Monte Carlo Engine

5,000 simulations per configuration. Each samples all nine risk distributions simultaneously:

$$\text{NPV}$$i = \sum{t=0}^{T} \frac{B_t \cdot \alpha_i(t) \cdot d_i^t - C_t \cdot o_i - R_t}{(1+r)^t}

where $\alpha_i(t)$ is the adoption S-curve with sampled ceiling and procurement delay, $d_i$ is the annual model degradation factor, $o_i$ is the cost overrun multiplier, and $R_t$ represents realized AI-specific costs (retraining, bias remediation, talent premium) in simulation $i$ at year $t$. Benefits are zeroed after any sampled defunding year. Adoption follows a logistic S-curve: $\alpha(t) = \frac{\alpha_{ceil}}{1 + e^{-0.8(t - t_{delay} - 3.5)}}$

User-Configurable Parameters

The tool accepts user-specified inputs:

All risk distributions can be overridden. Default distributions serve as empirically-informed starting points, not fixed assumptions.

Example Outputs

Example 1: Brazil Tax Administration

Inputs: Investment BRL 450M (estimated from comparable government tax technology procurement scales: HMRC Connect was reported at GBP 100M+, ATO analytics at AUD 200M+; adjusted for Brazil's scale and purchasing power). Annual benefit BRL 1,700M at full adoption (benchmark-discounted estimate from HMRC Connect, UK NAO HC 978, 2022-23). Discount rate 8%.

Sensitivity: Adoption ceiling > benefit uncertainty > procurement delay > model degradation > cost overrun.

Example 2: Saudi Arabia Municipal Services

Inputs: Investment SAR 280M (comparable municipal digitization scales, OECD 2023). Annual benefit SAR 470M (benchmarked against Singapore BCA Annual Report 2022/23). Discount rate 6%.

AI Risk Decomposition (Tool Feature)

Running each example with and without AI-specific risks illustrates the tool's decomposition capability. For these example inputs, AI-specific factors reduced median NPV by 12% (Brazil) and 9% (Saudi Arabia). Different input configurations would produce different decompositions — this is a feature of the tool, not a generalizable finding.

Discussion

Contribution Scope

This is a tool contribution. We provide: (1) an executable Monte Carlo framework, (2) a risk taxonomy distinguishing AI-specific from general government risks, and (3) default distributions calibrated from documented incidents rather than first principles. The tool enables scenario exploration — it does not predict outcomes.

Limitations

1. No ex-post validation. Testing against actual completed government AI projects is the necessary next step as such data becomes available.
2. Small incident database. AI bias distributions are calibrated from three documented cases. The distribution should be updated as more cases are documented.
3. Examples are not evidence. The Brazil and Saudi configurations demonstrate the tool, not the viability of those specific investments.
4. Input-output dependency. As sensitivity analysis confirms, outputs depend heavily on user-supplied benefit estimates. The tool quantifies this dependency but cannot resolve it.

Conclusion

We contribute an open-source Monte Carlo tool for government AI investment appraisal incorporating nine risk factors — five AI-specific — with default distributions calibrated from documented real-world incidents (Dutch childcare benefits scandal, Australia Robodebt, Michigan MiDAS) and ML engineering literature. The tool fills a practical gap: government analysts currently lack accessible methods to quantify AI-specific risks in investment cases.

References (all 2024 or earlier)

1. Standish Group, "CHAOS Report 2020," 2020.
2. Flyvbjerg B., "Survival of the Unfittest," Oxford Review of Economic Policy 25(3), 2009.
3. UK HM Treasury, "The Green Book," 2022.
4. OECD, "Government at a Glance 2023," 2023.
5. World Bank, "GovTech Maturity Index," 2022.
6. UK NAO, "HMRC Tax Compliance," HC 978, 2022-23.
7. Singapore BCA, "Annual Report 2022/2023," 2023.
8. Sculley D. et al., "Hidden Technical Debt in ML Systems," NeurIPS 28, 2015.
9. Obermeyer Z. et al., "Dissecting racial bias," Science 366(6464), 2019.
10. OECD, "Skills Outlook 2023," 2023.
11. Hadwick D. & Lan L., "Lessons from Dutch Childcare Benefits Scandal," SSRN, 2021.
12. Charette R.N., "Michigan's MiDAS Unemployment System: Algorithm Alchemy Created Lead, not Gold," IEEE Spectrum, 2018.
13. Australian Royal Commission into the Robodebt Scheme, "Report," Commonwealth of Australia, 2023.
14. Lu J. et al., "Learning under Concept Drift," IEEE TKDE 31(12), 2019.
15. US GAO, "AI in Government: Agencies Need to Address Risks," GAO-22-104714, 2022.
16. World Economic Forum, "Future of Jobs Report 2023," 2023.
17. IMF, "World Economic Outlook," October 2024.
18. IBGE, "Continuous PNAD," July 2024.
19. GASTAT, "Labour Force Survey Q3 2024," 2024.
20. "The Dutch Tax Authority Was Felled by AI — What Comes Next?," IEEE Spectrum, November 2022.