Algorithmic Fairness Debate
Daniel and Justin discuss the importance of looking at algorithmic fairness holistically, including examining the training data and input features. They highlight the neglect of certain dimensions of fairness in the lifecycle of the system and emphasize the need for a representative sample in data collection. The conversation provides valuable insights for practitioners and raises awareness about the ethical considerations in algorithmic decision-making.In this clip
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