Document Type
Article
Publication Title
Telematics and Informatics Reports
Abstract
Artificial intelligence (AI) is built into many products and has the potential to dramatically impact societies around the world. This short theoretical paper aims to provide a simple framework that might help us understand how the introduction and/or use of products with AI might influence the well-being of humans. It is proposed that considering the dynamic Interplay between variables stemming from Modality, Person, Area, Culture and Transparency categories will help to understand the influence of AI on well-being. The Modality category encompasses areas such as the degree of AI being interactive, informational versus actualizing, or autonomous. The Person variable contains variables such as age, gender, personality, technological self-efficacy, and perceived competence when interacting with AI, whereas the Area variable can comprise a certain product where AI is in-built or a certain domain where AI is used to make a difference (such as the health sector, military sector, education sector, etc.). The Culture variable is of importance to understand because cultural settings might shape attitudes towards AI. Finally, this might also be true for transparent AI (or understandable/explainable AI), with high degrees of transparency likely to elicit trust. The proposed model suggests that there is no easy answer when one seeks to understand the impact of AI on the world and humans. Only by considering a myriad number of variables in a model, summed up in the acronym IMPACT (Interaction/Interplay of Modality-Person-Area-Culture-Transparency), we might get closer to an understanding of how AI impacts individuals’ well-being.
DOI
10.1016/j.teler.2023.100112
Publication Date
3-2024
Keywords
Artificial Intelligence, Attitudes, Culture, Modality, Personality, Trust, XAI
Recommended Citation
C. Montag et al., "Considering the IMPACT framework to understand the AI-well-being-complex from an interdisciplinary perspective," Telematics and Informatics Reports, vol. 13, Mar 2024.
The definitive version is available at https://doi.org/10.1016/j.teler.2023.100112
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Uploaded: April 03, 2024