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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Arif Ali Khan
  • Muhammad Azeem Akbar
  • Mahdi Fahmideh
  • Peng Liang
  • Muhammad Waseem
  • Aakash Ahmad
  • Mahmood Niazi
  • Pekka Abrahamsson
<mark>Journal publication date</mark>1/12/2023
<mark>Journal</mark>IEEE Transactions on Computational Social Systems
Issue number6
Number of pages14
Pages (from-to)2971-2984
Publication StatusPublished
Early online date10/03/23
<mark>Original language</mark>English


Artificial intelligence (AI) solutions and technologies are being increasingly adopted in smart systems contexts; however, such technologies are concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies adhere to 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 randomly selected representative AI practitioners and lawmakers (e.g., AI engineers and lawyers) from 20 countries across five continents. To the best of our knowledge, this is the first empirical study that unveils 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 to be the most common AI ethics challenges. The impact analysis of the challenges across 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 and freedom) and challenges (e.g. lacking monitoring bodies and machine distortion). Our findings stimulate further research, particularly empowering existing capability maturity models to support ethics-aware AI systems&#x2019; development and quality assessment.