Implementing Responsible AI: A Life-Cycle Reference Model Based on FATES Principles

This paper presents a conceptual framework for the responsible use of Artificial Intelligence (AI), based on Fairness, Accountability, Transparency, Equity and Safety (FATES) principles. The FATES framework was directly influenced by global initiatives for designing, developing and deploying AI platforms and systems. As AI becomes increasingly embedded in various domains and sectors of industry and society, there is a critical need to monitor its responsible use. This conceptual paper first addresses the challenges of implementing AI for good, and for all. Next, it introduces the FATES principles for ethical AI practices. While discussing good practices for designing, developing and deploying AI responsibly, it synthesises insights from global public and private sector initiatives, differences between developed and developing economies and perspectives of academic scholars, policy makers, technology providers, mass media and, most importantly, the individuals and communities who adopt AI. This paper concludes with a Life-Cycle Reference Model for Responsible AI. We acknowledge that the reference model and its application to the AI implementation life-cycle are contextual. The specifics of the sector and use case determine much of assessing responsible use. Accordingly, we propose a general reference model that may be contextualised.

Authors

Institutions

Publication Details

Journal
Journal of Information & Knowledge Management
Published
2025-08-14
DOI
https://doi.org/10.1142/s0219649225500789
Citations
1
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Implementing Responsible AI: A Life-Cycle Reference Model Based on FATES Principles

Aijaz A. Shaikh, Samia Loucif, Ravi Sharma
1 citations
Journal of Information & Knowledge Management
Digital Transformation in Industry
1.24
article

Implementing Responsible AI: A Life-Cycle Reference Model Based on FATES Principles

Aijaz A. Shaikh, Samia Loucif, Ravi Sharma
article en
1 citations

Abstract

This paper presents a conceptual framework for the responsible use of Artificial Intelligence (AI), based on Fairness, Accountability, Transparency, Equity and Safety (FATES) principles. The FATES framework was directly influenced by global initiatives for designing, developing and deploying AI platforms and systems. As AI becomes increasingly embedded in various domains and sectors of industry and society, there is a critical need to monitor its responsible use. This conceptual paper first addresses the challenges of implementing AI for good, and for all. Next, it introduces the FATES principles for ethical AI practices. While discussing good practices for designing, developing and deploying AI responsibly, it synthesises insights from global public and private sector initiatives, differences between developed and developing economies and perspectives of academic scholars, policy makers, technology providers, mass media and, most importantly, the individuals and communities who adopt AI. This paper concludes with a Life-Cycle Reference Model for Responsible AI. We acknowledge that the reference model and its application to the AI implementation life-cycle are contextual. The specifics of the sector and use case determine much of assessing responsible use. Accordingly, we propose a general reference model that may be contextualised.

Journal of Information & Knowledge ManagementVol. 25(01)
Institute of Information and Computational Technologies (KZ), Zayed University (AE)
Openalex Percentile: Top 16%
Digital Transformation in Industry
1.24
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.

Implementing Responsible AI: A Life-Cycle Reference Model Based on FATES Principles — Aijaz A. Shaikh, Samia Loucif, et al. · Journal of Information & Knowledge Management (2025) | TGRS Research Map | TGRS