Deconstructing the AI Myth: Fallacies and Harms of Algorithmification

Authors

DOI:

https://doi.org/10.34190/ecel.23.1.2759

Keywords:

AI, AI hype, critical AI literacy, AI narrative

Abstract

We are experiencing a massive and volatile expansion of AI-based products and services. The current intermeshing of digital technologies, people, and society is shaping how we live and bringing algorithms to the forefront of decision making. The algorithmification of society and the narratives used to make it appear inevitable serve specific interests, mostly profitable for and controlled by few actors. It is not AI in itself, but the utilitarian sophistication of optimisation mechanisms and the power structures behind them that profit from controlling all that we do, when and how we do it, our behaviours, and even ourselves. In education, this is of serious concern as academia is gradually moving to uncertain dependencies on corporate interests. This paper calls for radical changes in dealing with the AI narratives that have monopolised recent public debates and discussions. It sheds light on the key terminology surrounding today’s AI algorithms and the technological background that makes them possible. It shows examples of the negative impacts and the implications of not addressing or ignoring certain issues, especially in education. This paper also suggests good practices through consistent advocacy, grounded materials, and critical work on digital literacy, particularly AI literacy.

Author Biographies

Dagmar Monett, Berlin School of Economics and Law

Computer Science Professor (Artificial Intelligence, Software Engineering) at the Berlin School of Economics and Law (HWR Berlin). Director of the Computer Science Division at the Department of Cooperative Studies Business and Technology and professional member of the ACM with over 35 years of research and teaching experience in different countries.

Bogdan Grigorescu, eBay

Technologist with extensive experience across industries. Bogdan has led specialist teams in implementing automation through AI at scale, delivering and operating enterprise platforms across geographies. Artificial Intelligence practitioner focused on standards and ethics for AI systems to help building "AI for good”. Background in electronics and telecommunication engineering.

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Published

2024-10-23