Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review

TL;DR

This paper investigates AI practitioners' views on ethics in AI through a grounded theory literature review (GTLR) of 38 empirical studies. It applies socio-technical grounded theory to code and synthesize data, resulting in a five‑category taxonomy of practitioner awareness, perception, need, challenge, and approach. The contributions include a practitioner‑centric taxonomy to guide ethics implementation and a research agenda with actionable recommendations for practitioners, managers, and organizations. The work highlights the gap between ethics principles and practice, reveals diverse practitioner perspectives, and emphasizes the need for tools, governance, and multidisciplinary collaboration to translate ethics into AI development.

Abstract

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems -- the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners' views on ethics in AI and analysed them to derive five categories: practitioner awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners' viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI.

Figures (6)

• Figure 1: Steps of the Grounded Theory Literature Review (GTLR) method with Socio-Technical Grounded Theory (STGT) for data analysis
• Figure 2: Example of a memo arising from the code ("principles vs practice gap") labeled [C1]
• Figure 3: Example of Socio-Technical Grounded Theory (STGT) data analysis hoda2021socio applied to primary studies ( : AI practitioner's quote; : Literature data)
• Figure 4: Taxonomy of Ethics in AI from Practitioners’ Viewpoints
• Figure 5: An Overview of the Aspects of Ethics in AI from AI Practitioners' Viewpoints