Introduction-Welcome!
Syllabus & Overview
Introduction-What is a predictive algorithm?
Examples of Predictive Algorithms
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How do you Build Predictive Algorithms?
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How do you Assess Predictive Algorithms
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Upsides and Takeaways
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Introduction-Fairness Lesson 1: What does it mean for a predictive algorithm to be fair?
Introduction and Statistical Parity
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Predictive Equality and Calibration
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Conflicts Between Definitions
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Takeaways
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Additional Reading [Optional]
Introduction-Fairness Lesson 2: Be precise about what you mean by bias!
Introduction
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Case study: many possible causes of bias
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Introduction-How will the lessons of this course apply to generative AI algorithms like ChatGPT?
Introduction
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Concerns & Takeaways
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Designing Algorithms-Fairness Lesson 3: Train and test your algorithm on diverse datasets
Introduction
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Principles of Ethical Data Collection & Takeaways
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Additional Reading [Optional]
Designing Algorithms-Fairness Lesson 4: Removing sensitive features won't automatically make your algorithm fair
Removing sensitive features won't automatically make your algorithm fair
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Including sensitive features may make your algorithm more fair
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Additional Reading [Optional]
Designing Algorithms-Fairness Lesson 5: Consider whether you're predicting what you want to be predicting
Intro and health risk prediction case study
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Further examples and takeaways
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Design a healthcare algorithm! [Required]
Additional Reading [Optional]
Documenting Algorithms-Fairness Lesson 6: Be clear about the intended uses of models and datasets
Intended Uses of Models and Datasets
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Documenting Intended Uses
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Additional Reading [Optional]
Documenting Algorithms-Fairness Lesson 7: Be wary of algorithms you can't examine
Introduction to Transparency and Interpretability
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Examples of Transparent Algorithms
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Examples of Non-Transparent Algorithms & Takeaways
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Additional Reading [Optional]
Algorithms in the hands of humans-Fairness Lesson 8: Consider how algorithms will guide human decision-making
Introduction to Algorithms Guiding Human Decision Making
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Are Criminal Justice Algorithms Inherently Unethical?
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Additional Ethical Dilemmas & Takeaways
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Additional Reading [Optional]
Algorithms in the hands of humans-Fairness Lesson 9: Consider how algorithms don't just predict the future; they shape it
Algorithms don't just predict the future; they shape it
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Additional Reading [Optional]
Algorithms in the hands of humans-Fairness Lesson 10: Compare to the human baseline - and remember that humans are unfair too
Compare Algorithms to the Human Baseline
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Takeaways & Course Summary
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Additional Reading [Optional]