MIT Introduction to Deep Learning 6.S191: Lecture 8
Algorithmic Bias and Fairness
Lecturer: Ava Soleimany
January 2021
For all lectures, slides, and lab materials: [ Ссылка ]
Lecture Outline
0:00 - Introduction and motivation
1:40 - What does "bias" mean?
4:22 - Bias in machine learning
8:32 - Bias at all stages in the AI life cycle
9:25 - Outline of the lecture
10:00 - Taxonomy (types) of common biases
11:29 - Interpretation driven biases
16:04 - Data driven biases - class imbalance
24:02 - Bias within the features
27:09 - Mitigate biases in the model/dataset
33:20 - Automated debiasing from learned latent structure
37:11 - Adaptive latent space debiasing
39:39 - Evaluation towards decreased racial and gender bias
41:00 - Summary and future considerations for AI fairness
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MIT 6.S191: AI Bias and Fairness
Теги
deep learningmitartificial intelligenceneural networksmachine learning6s1916.s191mit deep learningava soleimanysoleimanyalexander aminiaminitensorflowcomputer visiondeep mindopenaideeplearningaideep learning pythonalgorithmic biasbiasfairnessai biasracist aidiscriminationdebiasingmitigating biastimnit gerbrumargaret mitchellgoogle biasml biasreduce bias
