Biased Data Collection
The discussion highlights a critical concern in machine learning: the potential biases inherent in the data collection process. Andrew emphasizes that if the foundation of data is flawed, the outcomes will inevitably reflect those biases, raising ethical questions about the integrity of AI systems. This insight invites listeners to reconsider how data is gathered and the implications it has on technology.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)
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