Fairness in Machine Learning with Hanna Wallach - TWiML Talk #232

This article summarizes a discussion on fairness in machine learning, featuring Hanna Wallach, a Principal Researcher at Microsoft Research. The conversation explores how biases, lack of interpretability, and transparency issues manifest in machine learning models. It delves into the impact of human biases on data and the practical challenges of deploying "fair" ML models. The article highlights the importance of addressing these issues and provides resources for further exploration. The focus is on the ethical considerations and practical implications of bias in AI.

• •The discussion centers on the challenges of bias and lack of transparency in machine learning.
• •Human biases, both intentional and unintentional, can negatively impact data and model outcomes.
• •The article highlights resources for further study on fairness in machine learning.