This work proposes an ethical framework that highlights possible ethical risks in the design and use of deep-learning-based vision systems for monitoring infants' movements in neonatal intensive care units. We discuss biases and ways to mitigate them for promoting accountable systems in clinical practice.
Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring / Migliorelli, Lucia; Tiribelli, Simona; Cacciatore, Alessandro; Giovanola, Benedetta; Frontoni, Emanuele; Moccia, Sara. - In: COMPUTER. - ISSN 0018-9162. - 56:5(2023), pp. 84-93. [10.1109/MC.2023.3235987]
Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring
Lucia Migliorelli
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2023-01-01
Abstract
This work proposes an ethical framework that highlights possible ethical risks in the design and use of deep-learning-based vision systems for monitoring infants' movements in neonatal intensive care units. We discuss biases and ways to mitigate them for promoting accountable systems in clinical practice.File in questo prodotto:
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