The ethical implications of cloud automation: Balancing efficiency with accountability

This article delves into the ethical dimensions of cloud automation and AI-driven infrastructure management as organizations increasingly rely on these technologies to enhance operational efficiency. While cloud automation offers significant benefits in deployment speed, resource optimization, and cost reduction, it also introduces complex ethical challenges that require careful consideration. The article examines key ethical concerns, including accountability for automated system failures, transparency limitations in self-healing infrastructures, and algorithmic bias in resource allocation. Through analysis of industry examples and best practices, the article presents a comprehensive framework for integrating ethical principles into cloud automation strategies from the outset. The proposed "ethics by design" approach emphasizes clear governance structures, explainable systems, and continuous bias monitoring. A detailed case study of Capital One's cloud automation journey illustrates how organizations can successfully balance technological advancement with ethical responsibility. The article argues that treating ethics as a fundamental design parameter rather than a regulatory afterthought enables organizations to harness automation's full potential while building trust with stakeholders and meeting compliance requirements.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17240376
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The ethical implications of cloud automation: Balancing efficiency with accountability

Zenodo (CERN European Organization for Nuclear Research)
Technology Adoption and User Behaviour
article

The ethical implications of cloud automation: Balancing efficiency with accountability

article en

Abstract

This article delves into the ethical dimensions of cloud automation and AI-driven infrastructure management as organizations increasingly rely on these technologies to enhance operational efficiency. While cloud automation offers significant benefits in deployment speed, resource optimization, and cost reduction, it also introduces complex ethical challenges that require careful consideration. The article examines key ethical concerns, including accountability for automated system failures, transparency limitations in self-healing infrastructures, and algorithmic bias in resource allocation. Through analysis of industry examples and best practices, the article presents a comprehensive framework for integrating ethical principles into cloud automation strategies from the outset. The proposed "ethics by design" approach emphasizes clear governance structures, explainable systems, and continuous bias monitoring. A detailed case study of Capital One's cloud automation journey illustrates how organizations can successfully balance technological advancement with ethical responsibility. The article argues that treating ethics as a fundamental design parameter rather than a regulatory afterthought enables organizations to harness automation's full potential while building trust with stakeholders and meeting compliance requirements.

Zenodo (CERN European Organization for Nuclear Research)
Jawaharlal Nehru Technological University, Hyderabad (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 30%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.