Embedded Trustworthy AI for Healthcare

Abstract

The growing deployment of AI-assisted diagnostics on resource-constrained microcontrollers raises an underexplored question: do the memory and latency limitations of embedded hardware reshape the fairness–accuracy–privacy trade-offs that practitioners must navigate in healthcare applications? We present a controlled, multi-objective empirical study evaluating Gaussian noise injection, post-training INT8 quantization, and classification threshold calibration. Fairness and privacy interventions are evaluated on the Pima Indians Diabetes dataset (768 samples, age-stratified protected group) using a lightweight MLP and a logistic regression baseline; quantization efficiency is additionally validated on a larger hospital readmission dataset (∼100,000 samples, ∼154,800-parameter model) to characterise scale-dependent compression behaviour. The key findings are fourfold: (i) INT8 quantization efficiency is scale-dependent: no benefit and up to 67% fairness degradation at sub-300 parameters, versus 3.87× compression and 3.6× speedup at ∼155k parameters; (ii) low-magnitude noise (σ=0.05) is a safe privacy proxy with negligible accuracy cost; (iii) higher noise levels create a non-monotonic privacy–fairness tension, destabilising group-level fairness without predictably improving it; (iv) post-hoc threshold calibration to τ =0.7 reduces equalized odds gap by 18.4% relative at only 1.2 pp accuracy cost, out-performing all training-time interventions with zero embedded overhead. These findings show that embedded constraints do not introduce new fairness–accuracy trade-offs but shift design priorities toward post-deployment calibration.