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 as discussed in the episode Richard Socher — The Challenges of Making ML Work in the Real World and the clip Addressing AI Bias?
Are there biases in AI as discussed in the episode The Surprising Ways Algorithms Steer Your Life & How to Make Your Ideas Stick and the clip Algorithmic Bias Debate?
Can AI have biases as discussed in the episode Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 and the clip Bias in Machine Learning?