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CourseRES.EC-0019 lectures1 h 17 min

Exploring Fairness in Machine Learning for International Development

Dr. Richard Fletcher and others · Massachusetts Institute of Technology

Start with lecture 1 At MIT OpenCourseWare

About

In an effort to build the capacity of the students and faculty on the topics of bias and fairness in machine learning (ML) and appropriate use of ML, the MIT CITE team developed capacity-building activities and material. This material covers content through four modules that an be integrated into existing courses over a one to two week period.

Lectures

Module 1: Introduction to Ethics in Machine Learning

  1. Introduction to Ethics in Machine Learning 2 min · Video

Background

  1. Exploring Fairness in Machine Learning: Background 6 min · Video

USAID Appropriate Use Framework

  1. USAID Appropriate Use Framework, Exploring Fairness in Machine Learning 9 min · Video

Solar Lighting Example

  1. Solar Lighting Example, Exploring Fairness in Machine Learning 7 min · Video

Protected Attributes and "Fairness through Unawareness"

  1. Protected Attributes and "Fairness through Unawareness," Exploring Fairness in Machine Learning 7 min · Video

Fairness Criteria

  1. Fairness Criteria, Exploring Fairness in Machine Learning 8 min · Video

Case Study with Data: Mitigating Gender Bias on the UCI Adult Database

  1. Case Studies with Data: Mitigating Gender Bias on the UCI Adult Dataset 23 min · Video

Case Study on Pulmonary Health

  1. Pulmonary Health Case Study: Bias Exploration, Exploring Fairness in Machine Learning 6 min · Video

Case Study on Natural Language Processing: Identifying and Mitigating Unintended Demographic Bias in Machine Learning…

  1. Case Study: Identifying and Mitigating Unintended Demographic Bias in Machine Learning for NLP 14 min · Video

Syllabus and materials

At MIT OpenCourseWare, as the course publishes them.

Credits and licence

Institution
Massachusetts Institute of Technology
Department
Edgerton Center
Taught by
Dr. Richard Fletcher, Prof. Daniel Frey, Dr. Mike Teodorescu, Amit Gandhi, Audace Nakeshimana
Term
Spring 2020
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering, Teaching and learning
Source
MIT OpenCourseWare
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only. MIT OpenCourseWare's terms
Attribution
Dr. Richard Fletcher, Prof. Daniel Frey, Dr. Mike Teodorescu, Amit Gandhi, Audace Nakeshimana. Exploring Fairness in Machine Learning for International Development. Spring 2020. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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