Operationalizing AI Ethics in gem5 Simulation Work
AI tools are becoming part of everyday technical work. They can help generate code, explain documentation, draft configuration files, and accelerate debugging. But in research-oriented engineering work, speed is not enough. The result still has to be correct, reproducible, and defensible.
This is especially important in computer architecture simulation. In gem5 work, an AI-generated configuration, statistics modification, or memory-system change may not crash the program. Instead, it can silently change the meaning of an experiment. The simulation may still run, the output may still look clean, and the performance numbers may still appear impressive — but the result may no longer be valid.
This post documents my personal AI ethics criteria for using AI in gem5 simulation work. The goal is not to avoid AI entirely. The goal is to use AI responsibly while preserving correctness, reproducibility, and accountability.
Why This Matters
In one gem5 simulation task, I used an AI tool to help generate a Python configuration script for a memory-system experiment. The generated setup looked reasonable at first glance, but I hesitated because the configuration was complex and slightly opaque.
The concern was not that the script would fail immediately. The more serious risk was that it could contain a subtle logic error that would corrupt the experiment without producing an obvious runtime error. If I accepted the output without verification, I could end up trusting performance metrics that were based on an invalid configuration.
That moment shaped the core principle behind my framework:
I must be able to defend every AI-assisted code change, metric definition, and performance claim as my own verified work.
Personal AI Ethics Criteria for gem5 Simulation Work
Risk 1: Prefetch-Throttling Distortion
AI may suggest a prefetch-throttling rule that improves apparent IPC by suppressing too many prefetches.
The result may look better because memory traffic drops, but the policy may not actually be intelligent. It may simply under-prefetch, work only on a narrow benchmark set, and create a misleading conclusion about the design.
Risk 2: Statistics Collection Corruption
AI may help modify gem5 statistics collection while accidentally changing what is being counted.
This is dangerous because I could report prefetch accuracy, coverage, latency, or IPC numbers that are not comparable to the baseline. The experiment may appear rigorous, but the measurement method itself may be invalid.
Risk 3: Hidden Protocol or Scheduling Violation
AI-generated memory-controller or scheduling code may change request ordering, fairness, or protocol assumptions without making the violation obvious.
In that situation, I could report performance gains from a policy that is not architecturally valid, reproducible, or fair to certain request types. The numbers may improve, but the design itself may no longer represent a correct architecture experiment.
Core Value: Accountability
My core value is accountability.
I am responsible for every AI-assisted change I submit, run, analyze, or present. AI can help me move faster, but it cannot replace my responsibility to understand the control path, the metric definitions, and the experimental assumptions behind the result.
In research-oriented engineering work, the final claim belongs to the person making it — not to the tool that helped generate it.
Boundary for AI Use
I will never claim a performance improvement from AI-assisted gem5 changes unless I can verify two things.
First, the modified code must preserve architectural correctness.
Second, the reported metrics must be measured consistently against the baseline.
This boundary prevents me from treating AI output as evidence before it has passed through a verification process.
Standard Operating Procedure
Before every performance claim, I will trace each AI-assisted code or metric change against the relevant gem5 control path and statistics definitions, re-run the baseline and modified policy under the same configuration, and verify that IPC, latency, prefetch accuracy, and coverage are measured consistently.
Source and Technical Basis
This framework is informed by gem5’s statistics documentation. gem5 statistics are generated through SimObjects and dumped into output files such as stats.txt, which means metric names, locations, and definitions must be interpreted carefully before drawing conclusions.
It is also informed by gem5’s statistics API, which defines scalar, vector, distribution, and formula-style statistics. This reinforces an important research habit: custom or derived metrics must be understood before they are used as evidence.
Because gem5 experiments often involve multiple interacting components, a small AI-assisted change can affect more than one layer of the simulation. That is why my review process focuses not only on whether the code runs, but also on whether the experiment still measures what I claim it measures.
Reflection
The most important lesson from this exercise is that AI ethics does not have to remain abstract. In my work, ethics means building a repeatable verification habit around AI-assisted technical decisions.
For gem5 simulation, the question is not only:
“Did the AI generate working code?”
The better question is:
“Can I explain, verify, reproduce, and defend the result produced with that code?”
That question gives me a practical standard for using AI in my research and engineering workflow. It also gives future readers, collaborators, or reviewers a clearer reason to trust the claims I make.
AI can assist the work, but accountability remains mine.