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Inicio / Buscador / Salud y Medicina / Cuidado de la Salud
Arizona State University vía Coursera
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Practical Steps for Building Fair AI Algorithms

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  • Introduction
    • In this module, you'll learn the basic concepts this course relies on: what an algorithm is, and why fairness is tricky and subtle to define. We'll start by defining what a predictive algorithm even is, because this course is designed to be accessible to students who have never taken a computer science class. (If you have taken a previous class on predictive algorithms or machine learning, feel free to skip this section.)

      Then we'll jump right into fairness. This course will present ten practical fairness lessons, and in this module we'll discuss two of them. We'll also give a sneak preview of how the lessons of this course apply to generative AI models like ChatGPT.
  • Designing Algorithms
    • This module will cover fundamental lessons for designing fair algorithms: what data they should be trained on, what features they should use to predict, and what outcomes they should predict.
  • Documenting Algorithms
    • This module discusses the importance of documenting algorithms and datasets so they are used only in settings where they are appropriate.
  • Algorithms in the hands of humans
    • This module discusses the complex interplay between algorithmic predictions and human decisions.