Fairness and Calibration in Multilingual AI Incident Classification: A Cross-Site Validation Agenda

Abstract

Automated incident triage can treat languages unequally when high-severity examples are uncommon in some linguistic groups. The difficulty is particularly visible when a system is transferred to a new setting. The objective is to evaluate whether multilingual classifiers give equitable and calibrated risk estimates across language settings. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a stratified evaluation of annotated incident reports with language-family and chronological holdouts. Comparators will include a common multilingual model, per-language baselines and group-aware recalibration. Related enterprise-storage studies inform the assessment of operational reliability [1] and the deployment assumptions adopted for system-level comparison [2]. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are class-specific recall, expected calibration error, false escalation rates and analyst review burden. Potential biases include translated duplicates, dialect shifts, rare severe events and subgroup confidence intervals. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].