Open Access Mini Review Article ID: ARA-5-111

    Artificial intelligence: Explainability, ethical issues and bias

    Alaa Marshan*

    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. 


    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


    Case Reports

    Pinterest on ARA

    Help ? Google Reviews 11