SOURCE-LINKED INTELLIGENCE

How does human agency shape machine learning understanding?

CORDIS · observation · Publication date unknown

How does human agency shape machine learning understanding? Human agency over machine learning (ML) models refers to individuals' ability to monitor and act on the behavior of a trained ML model. In practice, ML experts have strong agency on ML systems, from choosing training data to monitoring performance metrics. Beyond them, few other stakeholders are granted the capacity to act on the system. HAMLU investigates how human agency over ML systems shapes our understanding of ML models. Do users better understand an ML model when they can actively explore the model's predictions and shape training data rather than passively review data or explanations? Addressing this challenge is crucial, as overlooking faulty behaviors from deployed ML models can harm European citizens in decision-making processes (e.g., recruitment, justice) and high-stake applications (e.g., self-driving vehicles, anomaly detection). At the int

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recordType

award

status

SIGNED

region

EU

value

184451.36

unit

EUR