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Architecting Ethical Machine Learning Infrastructure: a Systematic Framework for Technical Implementation

Year: 2024

Type: article

Abstract: This article presents a systematic framework for implementing ethical considerations within machine learning infrastructure, addressing the technical challenges of integrating fairness, explainability, and privacy protection mechanisms.The article examines the architectural components required for robust ethical ML systems, including automated bias detection pipelines, multi-level fairness monitoring systems, and parallel interpretation infrastructures for model explainability.The

Source: INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY

DOI: 10.34218/ijcet_15_06_160

Author: Aditya Singh

Institution: University of Wisconsin–Madison

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: diamond

OpenAlex ID: W4405854994

FWCI:

Cites: 8

Cited by:

Related to: 10