FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment

URL SCAN: FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment
FIRST LINE: Computer Science > Artificial Intelligence

THE DISSECTION
This paper builds an agentic control layer around model compression for FPGA deployment. Its real contribution is not a breakthrough in fairness or compression; it is workflow automation. A language-model planner searches measured pruning, quantization, and factorization configurations, then selects a model under explicit accuracy, fairness, and storage constraints.

The numbers show a useful engineering result: 59.54% lower inference tensor storage, a modest validation average-precision increase from 0.5141 to 0.5233, and a small EOpp reduction from 0.2251 to 0.2168. The claimed efficiency gain—7.33 rather than 12 candidate evaluations—means the planner reduces search overhead under the chosen stopping policies. This is configuration management with an agentic interface, not autonomous scientific intelligence.

THE CORE FALLACY
The central error is treating fairness as a measurable constraint that can be cleanly optimized alongside accuracy and hardware cost. The framework can reduce a selected disparity metric on Fitzpatrick-17k with VGG-11. It cannot establish that the resulting system is fair in any socially meaningful sense.

Equalized opportunity is one statistical target, not justice. Compression can alter errors unevenly across groups while improving the reported metric. A planner that finds a feasible point in a benchmark-defined constraint space has solved a search problem, not the legitimacy problem of deploying a classifier over human attributes. The paper mistakes metric compliance for governance.

Under the Discontinuity Thesis, the deeper fallacy is more consequential: it treats cheaper, more efficient AI deployment as a neutral technical improvement. It is not neutral. Lower storage and reduced search cost expand the number of systems that can be deployed on constrained hardware, which accelerates cognitive automation and makes automated judgment cheaper to replicate. The fairness layer is a steering wheel attached to an engine whose economic effect is further substitution of human labor.

HIDDEN ASSUMPTIONS

• That Fitzpatrick-17k and VGG-11 are adequate proxies for real deployment conditions.
• That the selected fairness metric captures the harms that matter.
• That group labels, measurement quality, and evaluation procedures are reliable and socially uncontested.
• That validation improvements survive distribution shift, adversarial use, and hardware-specific implementation effects.
• That fairness, accuracy, storage, and latency can be represented as stable, commensurable constraints.
• That requirement updates reflect legitimate user governance rather than changing targets after observing outcomes.
• That reporting residual violation is an adequate response when a request cannot be satisfied.
• That reducing candidate evaluations measures useful intelligence rather than simply exploiting a narrow, preconstructed search space.
• That deployment efficiency has no feedback effect on scale, surveillance, labor substitution, or institutional power.
• That fairness-aware compression remains relevant once larger models, better hardware, and automated deployment pipelines dominate the environment.

SOCIAL FUNCTION
Partial truth wrapped in transition management and ideological anesthetic.

The technical truth is real but narrow: measured feedback and explicit constraints can improve the selection of compressed models, and compression can make FPGA deployment cheaper. The anesthetic is the implication that adding fairness constraints to the pipeline meaningfully resolves the social problem created by automated classification and deployment.

This is also prestige signaling. “Agentic,” “fairness-aware,” and interactive requirement updates package a bounded optimization loop as a broader governance achievement. The framework does not transfer control of AI capital to affected groups. It gives existing owners a more efficient mechanism for deploying it while producing a defensible fairness report.

THE VERDICT
A competent compression-search workflow with modest empirical gains, inflated by agentic and fairness language. It does not challenge the Discontinuity Thesis; it strengthens it. The framework lowers the cost and friction of putting AI inference into hardware, while fairness becomes a constraint report attached to the deployment machinery. The paper manages the symptoms of automated judgment. It does not alter the ownership structure, the substitution pressure, or the collapse of productive human participation.