AI Literacy for the Common Good

Authors

DOI:

https://doi.org/10.34669/wi.wjds/4.1.5

Keywords:

AI Ethics, AI, Sustainable Artificial Intelligence, AI Literacy, Turing Galaxy

Abstract

Artificial Intelligence (AI) does not provide solutions to pressing social questions, such as those pertaining to a peaceful, sustainable, and socially acceptable world. However, when employed in a purposeful and critically reflective manner, it can assist in formulating more effective inquiries that can enable a better understanding of the terms “AI” and “common good.” Through implementation in response to sustainability issues and given its potential as an inclusive technology, AI could be a powerful and useful tool for the common good. Despite the possibility of useful machine learning applications in terms of a positive cost-benefit calculation for its life cycle energy and resources, the majority of AI is far too energy-hungry for model training and to scale inferences. Despite the considerable variation observed in terms of certain aspects, it is evident that AI is currently neither sustainable in itself nor primarily used for sustainability purposes to address the grand challenges of global society in a world characterized by rapid acceleration. This demands a critical understanding of how AI systems work to enable society to decide upon the areas in which we should, can, or even definitely must not use AI. Based on the UNESCO Framework for AI Competency and the Dagstuhl Declaration of the German Informatics Society, we advocate for a type of critical AI literacy that can be best taught through practical use, that is, “learning by making.” This approach leads to a concise overview of existing options that facilitate a more reflective approach to using and understanding AI, including its potential and limitations. We conclude with a practical example.

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Published

16-07-2024

How to Cite

Ullrich, S., & Messerschmidt, R. (2024). AI Literacy for the Common Good. Weizenbaum Journal of the Digital Society, 4(1). https://doi.org/10.34669/wi.wjds/4.1.5

Issue

Section

Research Papers