AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers

TL;DR

This empirical study explores AI practitioners' and lawmakers' perceptions of AI ethics, highlighting transparency, accountability, and privacy as key principles, while identifying major challenges like lack of legal frameworks and monitoring bodies.

Contribution

First empirical study comparing perceptions of AI practitioners and lawmakers on AI ethics principles and challenges across multiple countries.

Findings

Transparency, accountability, and privacy are the most critical AI ethics principles.

Lack of ethical knowledge and legal frameworks are the main challenges.

Conflict in practice significantly impacts AI ethics implementation.

Abstract

Artificial Intelligence (AI) solutions and technologies are being increasingly adopted in smart systems context, however, such technologies are continuously concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies bring ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 representative AI practitioners and lawmakers (e.g., AI engineers, lawyers) from twenty countries across five continents. To the best of our knowledge, this is the first empirical study that encapsulates the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found the most common AI ethics challenges. The impact analysis of the challenges across AI ethics principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness, freedom) and challenges (e.g. lacking monitoring bodies, machine distortion). Our findings stimulate further research, especially empowering existing capability maturity models to support the development and quality assessment of ethics-aware AI systems.