AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers
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 bri...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
30.06.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2207.01493 |