TY - GEN
T1 - A Systematic Review of Fairness in Artificial Intelligence Algorithms
AU - Xivuri, Khensani
AU - Twinomurinzi, Hossana
N1 - Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - Despite being the fastest-growing field because of its ability to enhance competitive advantage, there are concerns about the inherent fairness in Artificial Intelligence (AI) algorithms. In this study, a systematic review was performed on AI and the fairness of AI algorithms. 47 articles were reviewed for their focus, method of research, sectors, practices, and location. The key findings, summarized in a table, suggest that there is a lack of formalised AI terminology and definitions which subsequently results in contrasting views of AI algorithmic fairness. Most of the research is conceptual and focused on the technical aspects of narrow AI, compared to general AI or super AI. The public services sector is the target of most research, particularly criminal justice and immigration, followed by the health sector. AI algorithmic fairness is currently more focused on the technical and social/human aspects compared to the economic aspects. There was very little research from Asia, Middle East, Oceania, and Africa. The study makes suggestions for further research.
AB - Despite being the fastest-growing field because of its ability to enhance competitive advantage, there are concerns about the inherent fairness in Artificial Intelligence (AI) algorithms. In this study, a systematic review was performed on AI and the fairness of AI algorithms. 47 articles were reviewed for their focus, method of research, sectors, practices, and location. The key findings, summarized in a table, suggest that there is a lack of formalised AI terminology and definitions which subsequently results in contrasting views of AI algorithmic fairness. Most of the research is conceptual and focused on the technical aspects of narrow AI, compared to general AI or super AI. The public services sector is the target of most research, particularly criminal justice and immigration, followed by the health sector. AI algorithmic fairness is currently more focused on the technical and social/human aspects compared to the economic aspects. There was very little research from Asia, Middle East, Oceania, and Africa. The study makes suggestions for further research.
KW - AI
KW - Algorithms
KW - Bias
KW - Ethics
KW - Fairness
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85115174333&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-85447-8_24
DO - 10.1007/978-3-030-85447-8_24
M3 - Conference contribution
AN - SCOPUS:85115174333
SN - 9783030854461
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 271
EP - 284
BT - Responsible AI and Analytics for an Ethical and Inclusive Digitized Society - 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021, Proceedings
A2 - Dennehy, Denis
A2 - Griva, Anastasia
A2 - Pouloudi, Nancy
A2 - Dwivedi, Yogesh K.
A2 - Dwivedi, Yogesh K.
A2 - Pappas, Ilias
A2 - Pappas, Ilias
A2 - Mantymaki, Matti
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021
Y2 - 1 September 2021 through 3 September 2021
ER -