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Ethics of AI: A Systematic Literature Review of Principles and Challenges. / Khan, Arif Ali; Badshah, Sher; Liang, Peng et al.
Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022. New York: The Association for Computing Machinery, 2022. p. 383-392 (ACM International Conference Proceeding Series).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Khan, AA, Badshah, S, Liang, P, Waseem, M, Khan, B
, Ahmad, A, Fahmideh, M, Niazi, M & Akbar, MA 2022,
Ethics of AI: A Systematic Literature Review of Principles and Challenges. in
Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022. ACM International Conference Proceeding Series, The Association for Computing Machinery, New York, pp. 383-392, 26th ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022, Gothenburg, Sweden,
13/06/22.
https://doi.org/10.1145/3530019.3531329APA
Khan, A. A., Badshah, S., Liang, P., Waseem, M., Khan, B.
, Ahmad, A., Fahmideh, M., Niazi, M., & Akbar, M. A. (2022).
Ethics of AI: A Systematic Literature Review of Principles and Challenges. In
Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022 (pp. 383-392). (ACM International Conference Proceeding Series). The Association for Computing Machinery.
https://doi.org/10.1145/3530019.3531329Vancouver
Author
Bibtex
@inproceedings{627d6aa8c0c6404d94505c5caa4e244e,
title = "Ethics of AI: A Systematic Literature Review of Principles and Challenges",
abstract = "Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.",
keywords = "AI Ethics, Challenges, Machine Ethics, Principles, Systematic Literature Review",
author = "Khan, {Arif Ali} and Sher Badshah and Peng Liang and Muhammad Waseem and Bilal Khan and Aakash Ahmad and Mahdi Fahmideh and Mahmood Niazi and Akbar, {Muhammad Azeem}",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 26th ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022 ; Conference date: 13-06-2022 Through 15-06-2022",
year = "2022",
month = jun,
day = "13",
doi = "10.1145/3530019.3531329",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "The Association for Computing Machinery",
pages = "383--392",
booktitle = "Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022",
}
RIS
TY - GEN
T1 - Ethics of AI
T2 - 26th ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022
AU - Khan, Arif Ali
AU - Badshah, Sher
AU - Liang, Peng
AU - Waseem, Muhammad
AU - Khan, Bilal
AU - Ahmad, Aakash
AU - Fahmideh, Mahdi
AU - Niazi, Mahmood
AU - Akbar, Muhammad Azeem
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/13
Y1 - 2022/6/13
N2 - Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.
AB - Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.
KW - AI Ethics
KW - Challenges
KW - Machine Ethics
KW - Principles
KW - Systematic Literature Review
U2 - 10.1145/3530019.3531329
DO - 10.1145/3530019.3531329
M3 - Conference contribution/Paper
AN - SCOPUS:85132446173
T3 - ACM International Conference Proceeding Series
SP - 383
EP - 392
BT - Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022
PB - The Association for Computing Machinery
CY - New York
Y2 - 13 June 2022 through 15 June 2022
ER -
