“Don’t let me be misunderstood”
Critical AI literacy for the constructive use of AI technology
Keywords:deep automation bias, AI assessment, machine learning, uncertainty, awareness
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.
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Copyright (c) 2021 Stefan Strauß
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles in TATuP - Journal for Technology Assessment in Theory and Practice are published under the Creative Commons Licence CC BY 4.0.