Strategic Motivators for Ethical AI System Development: An Empirical and Holistic Model

TL;DR

This paper identifies and ranks key motivators for ethical AI development using a comprehensive model, emphasizing human resource and coordination as primary drivers to guide organizations in responsible AI practices.

Contribution

It introduces an empirical, holistic model of AI ethical motivators, combining literature review and quantitative analysis to prioritize factors influencing responsible AI development.

Findings

Identified 20 key motivators grouped into 8 categories.

Human Resource and Coordination are primary influencers.

Top-ranked motivators include team diversity and AI governance.

Abstract

Artificial Intelligence (AI) presents transformative opportunities for industries and society, but its responsible development is essential to prevent unintended consequences. Ethically sound AI systems demand strategic planning, strong governance, and an understanding of the key drivers that promote responsible practices. This study aims to identify and prioritize the motivators that drive the ethical development of AI systems. A Multivocal Literature Review (MLR) and a questionnaire-based survey were conducted to capture current practices in ethical AI. We applied Interpretive Structure Modeling (ISM) to explore the relationships between motivator categories, followed by MICMAC analysis to classify them by their driving and dependence power. Fuzzy TOPSIS was used to rank these motivators by importance. Twenty key motivators were identified and grouped into eight categories: Human Resource, Knowledge Integration, Coordination, Project Administration, Standards, Technology Factor, Stakeholders, and Strategy & Matrices. ISM results showed that 'Human Resource' and 'Coordination' heavily influence other factors. MICMAC analysis placed categories like Human Resource (CA1), Coordination (CA3), Stakeholders (CA7), and Strategy & Matrices (CA8) in the independent cluster, indicating high driving but low dependence power. Fuzzy TOPSIS ranked motivators such as promoting team diversity, establishing AI governance bodies, appointing oversight leaders, and ensuring data privacy as most critical. To support ethical AI adoption, organizations should align their strategies with these motivators and integrate them into their policies, governance models, and development frameworks.