[Paper Review] AI Ethics in Industry: A Research Framework

Ville Vakkuri, Kai‐Kristian Kemell+1 more|arXiv (Cornell University)|Oct 28, 2019

Ethics and Social Impacts of AI19 references18 citations

TL;DR

This paper proposes a research framework for implementing AI ethics in industrial settings, drawing from empirical case studies to structure ethical considerations throughout AI development. It provides a practical, evolving model for researchers and practitioners to study and operationalize ethics in real-world AI systems, with initial validation from industrial applications.

ABSTRACT

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Motivation & Objective

• To address the growing need for practical frameworks to implement AI ethics in industrial contexts.
• To identify and structure key ethical challenges in AI system development through real-world case studies.
• To support empirical research on how organizations operationalize ethics in AI development processes.
• To provide a foundation for ongoing refinement of ethical implementation based on industrial practice.
• To bridge the gap between theoretical AI ethics and actionable, context-specific implementation in industry.

Proposed method

• Developed through a multiple-case study approach in industrial settings focusing on AI system development.
• Identifies key dimensions of AI ethics, including fairness, transparency, accountability, and human oversight.
• Structures the framework around lifecycle phases of AI development, from design to deployment.
• Integrates stakeholder perspectives (engineers, managers, ethicists) to reflect real-world complexity.
• Uses iterative feedback from industrial applications to refine and validate the framework.
• Employs qualitative analysis of interviews and documentation to extract ethical implementation patterns.

Experimental results

Research questions

• RQ1 How do industrial organizations currently implement ethics in AI system development?
• RQ2 What are the key challenges and enablers for embedding ethical considerations in AI development processes?
• RQ3 How can a research framework be structured to support empirical studies on AI ethics in industry?
• RQ4 What role do different stakeholders play in shaping ethical AI practices within organizations?
• RQ5 How does the framework evolve through practical application in diverse industrial contexts?

Key findings

• The framework identifies critical phases in AI development where ethical considerations must be systematically addressed.
• Stakeholder collaboration—especially between technical teams and ethicists—is essential for effective implementation.
• Organizations face significant challenges in operationalizing abstract ethical principles due to lack of clear guidelines.
• Transparency and accountability mechanisms are inconsistently applied across industrial AI projects.
• The framework demonstrates adaptability through iterative feedback from industrial case studies.
• Initial validation shows the framework supports structured, context-aware ethical decision-making in AI development.