Building Connections Through Open Research: Meta’s Joelle Pineau
Me, Myself, and AI
Exploring Bias in Machine Learning Models and Data Sets
Bias in machine learning models can be attributed to both the training data and the models themselves. The data sets often contain biases reflecting societal unfairness and discrimination, leading the models to enhance these biases. Machine learning techniques tend to interpolate data well but struggle with extrapolation, resulting in predictions leaning towards the norm of the data distribution. The evaluation of models typically focuses on aggregate statistics rather than a more detailed analysis of performance across different groups. To mitigate biases, a more rigorous and thoughtful approach is necessary to ensure AI is used towards creating a fairer and more equitable society.
00:00
Transcript
Play full episode
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
