Rethinking Artificial Intelligence: Algorithmic Bias and Ethical Issues| Rage Against the Artificial Intelligence? Understanding Contextuality of Algorithm Aversion and Appreciation

Tessa Oomen, João Gonçalves, Anouk Mols


People tend to be hesitant toward algorithmic tools, and this aversion potentially affects how innovations in artificial intelligence (AI) are effectively implemented. Explanatory mechanisms for aversion are based on individual or structural issues but often lack reflection on real-world contexts. Our study addresses this gap through a mixed-method approach, analyzing seven cases of AI deployment and their public reception on social media and in news articles. Using the Contextual Integrity framework, we argue that most often it is not the AI technology that is perceived as problematic, but that processes related to transparency, consent, and lack of influence by individuals raise aversion. Future research into aversion should acknowledge that technologies cannot be extricated from their contexts if they aim to understand public perceptions of AI innovation.


artificial intelligence, algorithm aversion, algorithm appreciation, public perceptions, Contextual Integrity, mixed methods

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