There is no doubt that Artificial Intelligence (AI) is a topic that is attracting increasing attention from different communities, business and academic. AI adoption and implementation is faced by the difficulty of interpreting and trusting the outcomes of AI algorithms. Several ethical issues related to AI adoption such as algorithms and data bias are among the factors that hinder AI adoption by the business world. This study aims to highlight and classify the most important research that have been published on AI explainability and ethical issues. The main finding from this research refer to the necessity of forming proper comprehension of advantages and disadvantages offered by Explainable AI techniques. This work concludes that the interpretability of AI models needs to be investigated using innovative approaches such as data visualisation in conjunction with the requirements and constraints associated with data confidentiality and bias as well as the auditability, fairness and accountability of the AI model.
Keywords:
Published on: Aug 3, 2021 Pages: 34-37
Full Text PDF
Full Text HTML
DOI: 10.17352/ara.000011
CrossMark
Publons
Harvard Library HOLLIS
Search IT
Semantic Scholar
Get Citation
Base Search
Scilit
OAI-PMH
ResearchGate
Academic Microsoft
GrowKudos
Universite de Paris
UW Libraries
SJSU King Library
SJSU King Library
NUS Library
McGill
DET KGL BIBLiOTEK
JCU Discovery
Universidad De Lima
WorldCat
VU on WorldCat
PTZ: We're glad you're here. Please click "create a new query" if you are a new visitor to our website and need further information from us.
If you are already a member of our network and need to keep track of any developments regarding a question you have already submitted, click "take me to my Query."