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Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., & Vidal, M.-E., et al.. (2020). Bias in data-driven artificial intelligence systems—an introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356. 
Resource type: Journal Article
BibTeX citation key: Ntoutsi2020
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Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, Ethics, General
Subcategories: Augmented cognition, Big data, Decision making, Human decisionmaking, Machine learning, Psychology of human-AI interaction
Creators: Fafalios, Gadiraju, Iosifidis, Krasanakis, Nejdl, Ntoutsi, others, Papadopoulos, Ruggieri, Turini, Vidal
Collection: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their deci- sions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond tradi- tional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame.

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