The Determinism Challenge: Making AI Reliable for Government Services

Why Determinism Matters in AI — and Why It’s Difficult 

For all the promise of artificial intelligence, one challenge continues to trip up even the most advanced systems: repeatability. If you ask an AI engine the same question twice and get two slightly different answers, that might not seem like a big deal — unless that answer determines whether a family receives food assistance, or whether a contractor is licensed to work, what the tax rate is.  You got the idea.  This can be a big problem for government.   

In public-sector settings where fairness, accountability, and transparency are non-negotiable, the expectation is simple: identical inputs should always produce identical outputs. Yet modern AI systems, especially large language models (LLMs), are inherently probabilistic. They generate words not by following a static rulebook, but by predicting the next most likely token based on context. That makes them powerful — and unpredictable. 

As governments explore how to use AI responsibly in eligibility systems, case evaluations, and service automation, determinism — the ability to produce consistent and explainable outcomes — becomes more than a technical curiosity. It becomes a matter of public trust. 

The Nature of AI Variability 

To understand why AI outputs vary, it helps to unpack how modern LLMs generate text. At their core, models like GPT or Claude work by sampling probabilities: at each step, they choose from a distribution of possible next words. Even small differences in parameters, prompt phrasing, or hidden system settings can shift the result. 

None of these factors are “bugs.” They are fundamental design features of generative AI — the same features that make it flexible, conversational, and capable of synthesizing new insights. But when those qualities meet government processes that require traceable decisions, tension arises

Why This Matters for Enterprise and Government AI 

In consumer applications, a little unpredictability adds value. You wouldn’t want every AI-generated social post, story draft, or creative brainstorm to sound exactly the same. But in regulated domains — particularly government services — unpredictability can be catastrophic. 

Imagine an eligibility engine that interprets income verification one way on Monday and another way on Friday. Or a case scoring model that assigns different confidence ratings to the same report under slightly different conditions. These inconsistencies undermine the very foundation of trustworthy AI: fairness, accountability, and explainability. 

Determinism isn’t just a technical preference — it’s a compliance requirement. Many states are adopting responsible AI frameworks aligned with federal standards such as the NIST AI Risk Management Framework and the White House Blueprint for an AI Bill of Rights. Both emphasize reproducibility and traceability as core principles. 

In practical terms, that means every AI decision that affects a citizen must be: 

• Reconstructable — the same inputs produce the same outputs. 

• Auditable — the steps leading to the outcome can be reviewed. 

• Governed — parameters and model versions are documented and controlled. 

The Construct of a Solution 

Solving this doesn’t mean eliminating all variability — it means containing it. Deterministic AI design is about creating bounded creativity: allowing generative models to reason, summarize, and infer within clearly defined guardrails. 

At Servos, we often think of this in layered terms — where each layer tightens control over how outputs are produced: 

A Glimpse Ahead 

This article lays the foundation — why determinism matters, where variability originates, and what principles guide a stable solution. 

In Part 2, we’ll move from concept to implementation: 

• How to use random seed initialization in code. 

• How to enforce schema validation in LLM APIs. 

• Recursive result checking.  (I may have made that term up.  But it works.  😊 )  

• How to build a reproducibility pipeline with audit logs and change tracking. 

Building deterministic AI isn’t about limiting innovation. It’s about ensuring that every AI-powered decision stands up to the same scrutiny we expect from any other system that impacts people’s lives. 

Pat Snow serves as Vice President of State and Local Government Strategy at Servos, following his retirement as CTO of the State of South Dakota in June 2024. During his 28-year career in state government, Pat established South Dakota as a national leader in consolidated IT infrastructure and digital service delivery. At Servos, he continues to drive digital transformation in the public sector, helping agencies deliver more efficient and accessible services through the ServiceNow platform.