IJMB Journal – Abstracts
International Journal of Management and Business
IJMB Volume X, Special Edition
Inherent Negatives of Artificial Intelligence
Laura Meléndez
Rutgers University
E-mail: mmelendez.laura@gmail.com
ABSTRACT
This paper analyses artificial intelligence (AI) technology from a business social responsibility perspective. It applies various corporate social responsibility frameworks, including Rubin and Carmichael’s (2018) inherent negatives concept, stakeholder theory, and triple bottom line theory, to identify aspects of the AI technology that pose potential risks to societal order. The paper uses textual and context review and analysis of literature, studies and current AI news reports, as well as analysis of video content and speeches in the public domain, with focus on the United States (US). The inherent negatives identified are: jobs elimination due to the automation of mechanical tasks, dehumanization of work, algorithmic bias, insufficient data sets, lack of algorithm transparency and complex systems interdependence. The paper defines these inherent negatives, provides evidence of existence and explores business social responsibility implications, including the role of philanthropy. The discussion then turns to current governmental action that addresses the AI space, explaining the European Union’s General Data Protection Regulation (GDRP) as well as other AI reports in the US and the United Kingdom. It also addresses some of the recently formed non-governmental organizations (NGOs), some born out of philanthropic efforts, that are working to balance AI’s inherent negatives. Lastly, this paper outlines areas for future AI business social responsibility study.
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