Practical Steps for Building Fair Algorithms
- Offered byCoursera
Practical Steps for Building Fair Algorithms at Coursera Overview
Duration | 5 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Practical Steps for Building Fair Algorithms at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 13 quizzes & 4 assignments
Practical Steps for Building Fair Algorithms at Coursera Course details
- What you'll learn
- Understand widely used definitions of fairness and bias
- Master principles to follow when training models
- Design a healthcare algorithm
- Reason about challenging algorithmic fairness dilemmas
- Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas
- This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes
- It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT
- You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness
- This course is aimed at a broad audience of students in high school or above who are interested in computer science and algorithm design
- It will not require you to write code, and relevant computer science concepts will be explained at the beginning of the course
- The course is designed to be useful to engineers and data scientists interested in building fair algorithms; policy-makers and managers interested in assessing algorithms for fairness; and all citizens of a society increasingly shaped by algorithmic decision-making
Practical Steps for Building Fair Algorithms at Coursera Curriculum
Introduction
Examples of Predictive Algorithms
How do you Build Predictive Algorithms?
How do you Assess Predictive Algorithms
Upsides and Takeaways
Introduction and Statistical Parity
Predictive Equality and Calibration
Conflicts Between Definitions
Takeaways
Introduction
Case study: many possible causes of bias
Introduction
Concerns & Takeaways
Syllabus & Overview
Additional Reading [Optional]
Consent to participate in a research study
Fairness Definitions Quiz #1
Fairness Definitions Quiz #2
Module 1 Assessment
Designing Algorithms
Introduction
Principles of Ethical Data Collection & Takeaways
Removing sensitive features won't automatically make your algorithm fair
Including sensitive features may make your algorithm more fair
Intro and health risk prediction case study
Further examples and takeaways
Additional Reading [Optional]
Additional Reading [Optional]
Design a healthcare algorithm! [Required]
Additional Reading [Optional]
Pre-Lesson 3 Quiz
Post-Lesson 3 Quiz
Pre-Lesson 4 Quiz
Post-Lesson 4 Quiz
Module 2 Assessment
Documenting Algorithms
Intended Uses of Models and Datasets
Documenting Intended Uses
Introduction to Transparency and Interpretability
Examples of Transparent Algorithms
Examples of Non-Transparent Algorithms & Takeaways
Additional Reading [Optional]
Additional Reading [Optional]
Pre-Lesson 7 Quiz
Post-Lesson 7 Quiz
Module 3 Assessment
Algorithms in the hands of humans
Introduction to Algorithms Guiding Human Decision Making
Are Criminal Justice Algorithms Inherently Unethical?
Additional Ethical Dilemmas & Takeaways
Algorithms don't just predict the future; they shape it
Compare Algorithms to the Human Baseline
Takeaways & Course Summary
Additional Reading [Optional]
Additional Reading [Optional]
Additional Reading [Optional]
Ethical Dilemmas Pre-Quiz
Ethical Dilemmas Post-Quiz
Pre-Lesson 10 Quiz
Post-Lesson 10 Quiz
Module 4 Assessment
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