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Introduction
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Classroom Contents
Bias in the Vision and Language of Artificial Intelligence
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- 1 Introduction
- 2 What do you see
- 3 A riddle
- 4 Gender norms
- 5 Outcomes properties
- 6 AI pipeline
- 7 Human biases
- 8 Biased data representation
- 9 Bias network effect
- 10 Bias amplifies injustice
- 11 Bias in predictive policing
- 12 Bias in computer vision
- 13 Predicting criminality
- 14 Automated inference on criminality
- 15 Predicting homosexuality
- 16 What now
- 17 De disaggregated evaluation
- 18 How this works
- 19 Intersection
- 20 Confusion Matrix
- 21 Precision Force
- 22 Acceptable tradeoffs
- 23 Privacy and images
- 24 False negatives
- 25 Data constraints
- 26 Google Translate
- 27 Unjust outcomes
- 28 Handle your data
- 29 Tools
- 30 Documentation
- 31 Conclusion
- 32 Questions
- 33 All data is biased
- 34 Inclusive Images
- 35 Standards
- 36 Synthetic Data
