Data Bias Challenges
The discussion highlights the complexities of model misspecification and its impact on machine learning outcomes, particularly in contexts like banking. As self-selection bias and various fairness constraints intertwine, the challenge lies in balancing these competing factors without a clear ground truth. This makes the task of parameter recovery increasingly elusive in a machine learning framework.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)
Related Questions
Are there biases in AI?
Are there biases in AI as discussed in the episode Richard Socher — The Challenges of Making ML Work in the Real World and the clip Addressing AI Bias?
Can AI have biases as discussed in the episode Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 and the clip Bias in Machine Learning?