Ethical AI Audits for Observability Systems: Ensuring Equitable Resilience in Cloud Infrastructure

Abstract

The integration of artificial intelligence into cloud observability systems has revolutionized infrastructure monitoring while simultaneously introducing equity challenges that disproportionately affect underserved populations. These AI-driven systems, predominantly trained on data from high-density urban environments, frequently exhibit biased performance that manifests as prolonged resolution times and decreased detection accuracy in rural and developing regions. As cloud infrastructure increasingly underpins critical services such as healthcare, education, and financial systems, these disparities represent significant barriers to digital inclusion for billions of users worldwide. This article presents ethical AI auditing as a comprehensive framework to identify, quantify, and mitigate these biases through three key components: synthetic data generation to represent underserved scenarios, fairness metrics implementation to establish quantitative benchmarks, and bias mitigation techniques to correct algorithmic disparities. Case studies across European cloud providers, global content delivery networks, and emergency response systems demonstrate substantial improvements in service equity following audit implementation. Despite challenges related to resource requirements, performance trade-offs, privacy considerations, and evolving regulatory landscapes, ethical AI audits offer a viable path toward equitable cloud resilience that benefits both marginalized users and service providers through expanded market reach, enhanced reputation, and improved regulatory compliance.