# Exploring Fairness in Machine Learning for International Development

- 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](https://ocw.mit.edu/courses/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/)
- Licence: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (Non-commercial use only.)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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

1. [Introduction to Ethics in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/9893547be0ba), 2 min (Video)
2. [Exploring Fairness in Machine Learning: Background](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/d31a1676da07), 6 min (Video)
3. [USAID Appropriate Use Framework, Exploring Fairness in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/060c84a9565c), 9 min (Video)
4. [Solar Lighting Example, Exploring Fairness in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/bcc849d7e4dd), 7 min (Video)
5. [Protected Attributes and "Fairness through Unawareness," Exploring Fairness in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/2af21fe38716), 7 min (Video)
6. [Fairness Criteria, Exploring Fairness in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/503e4820b2a6), 8 min (Video)
7. [Case Studies with Data: Mitigating Gender Bias on the UCI Adult Dataset](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/b3e1ecaa2b97), 23 min (Video)
8. [Pulmonary Health Case Study: Bias Exploration, Exploring Fairness in Machine Learning](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/149a73df8409), 6 min (Video)
9. [Case Study: Identifying and Mitigating Unintended Demographic Bias in Machine Learning for NLP](https://invisiblenetwork.ai/learn/mit/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/54c39f6d52b6), 14 min (Video)