Abstract
Research and development as well as societal debates on the risks of artificial intelligence (AI) often focus on crucial but impractical ethical issues or on technocratic approaches to managing societal and ethical risks with technology. To overcome this, more practical, problem-oriented analytical perspectives on the risks of AI are needed. This article proposes an approach that focuses on a meta-risk inherent in AI systems: deep automation bias. It is assumed that the mismatch between system behavior and user practice in specific application contexts due to AI‑based automation is a key trigger for bias and other societal risks. The article presents the main factors of (deep) automation bias and outlines a framework providing indicators for the detection of deep automation bias ultimately triggered by such a mismatch. This approach intends to strengthen problem awareness and critical AI literacy and thereby create some practial use.
Highlights
The hype around artificial intelligence (AI) is yet unbroken
This article proposes an approach that focuses on a meta-risk inherent in AI systems: deep automation bias
It is assumed that the mismatch between system behavior and user practice in specific application contexts due to AI‐based automation is a key trigger for bias and other societal risks
Summary
Abstract Research and development as well as societal debates on the risks of artificial intelligence (AI) often focus on crucial but impractical ethical issues or on technocratic approaches to managing societal and ethical risks with technology. The article presents the main factors of (deep) automation bias and outlines a framework providing indicators for the detection of deep automation bias triggered by such a mismatch. This approach intends to strengthen problem awareness and critical AI literacy and thereby create some practial use. Dieser Ansatz will durch Stärkung von Problembewusstsein und kritischer KI-Kompetenz auch praktischen Nutzen erzielen. Keywords deep automation bias, AI assessment, machine learning, uncertainty, awareness
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