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Operationalizing Ethical AI in Financial DevOps: Balancing Intelligent Automation with Fairness and Accountability

Author(s) Amol Diwakar Agade, Samta Balpande
Country United States
Abstract In modern technological world, most financial institutions understand the role of Artificial Intelligence that its playing in transforming the technological space. Similarly, these institutions are integrating AI into DevOps and AIops to boost the release confidence and decrease the mean to recover from the failures. Once these AIOps models becomes the part of delivery loop, the decision made by this models can create the unequal outcomes, confuse the accountability and models can quickly fall out of the compliance due to frequent change in the requirements. This paper outlines the practical plan for implementing the ethical AI in the delivery and operational toolchain of DevOps in a regulated Industries like Finance, Energy and Nuclear sector. We have introduced the reference architecture that treats provenance, explainability, auditability and fairness as important requirements and enforce these standards via policy as a code, evidence backed by telemetry, and runtime guardrails. In finance industry the implementation of AIOps, these policy connects all AI risk governance such as NIST AI RMF, ISO guidance, financial sector resilience expectation like DORA, supply chain security practices like SSDF, SLSA into a unified control as a code model. We have also defined quantitative release risk scoring, fairness and drift checkpoints that supports a near real time supervisory readiness without slowing down delivery speed. Figures, data schemas, evaluation metrics and tables are provided in the table to aid direct adoption by platform and reliability engineering teams.
Keywords DevOps, AIOps, MLOps, ethical AI, financial services, model governance, fairness, explainability, audit evidence, policy-as-code.
Field Engineering
Published In Volume 6, Issue 9, September 2025
Published On 2025-09-08
DOI https://doi.org/10.70528/IJLRP.v6.i9.1992
Short DOI https://doi.org/hbtqj2
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