• How to evaluate predictor impact in black box models using interpretability methods

• How to explain the average contribution of features to predictions and the contribution of individual feature values to individual predictions

• How to Assess the performance of models with metrics to measure bias and unfairness

• How to describe potential ethical issues that can arise with image and text data, and how to address them

• How to donduct an audit of a data science project from an ethical standpoint to identify possible harms and potential areas for bias mitigation or harm reduction

In this course we will mostly be addressing things the data scientist can do to ensure that their projects and solutions are designed and implemented responsibly. We will primarily focus on issues of bias and unfairness across protected groups.

This course is arranged in 4 modules. We estimate that you will need to spend at least 5 hours per week. The course is self-paced, so you have the flexibility to complete modules in your own time. ****

Week 1 – Audit and Remediation

• Videos:

  • Introduction

  • Audit and Remediation

  • Confusion Matrix

  • Beyond Classic Bias

  • Regression

• Knowledge Checks

• Lab 1 (for verified users only)

• Discussion Prompt (for verified users only)

Week 2 – Interpretability in Practice

• Videos:

  • Interpretability

  • Global Interpretability

  • Fidelity, Robustness, Caveats

  • Local Interpretability Methods

• Knowledge Checks

• Reading

• Lab 2 (for verified users only)

• Discussion Prompt (for verified users only)

Week 3 – Image and Text Data

• Videos:

  • Image and Text Data

  • Neural Net Interpretability

• Knowledge Checks

• Readings

• Lab 3 (for verified users only) - will need gmail account for this lab

• Discussion Prompt (for verified users only)

Week 4 – Tools and Documentation

• Videos:

  • Tools and Documentation
  • Readings
  • Knowledge Checks
  • Quiz (for verified users only)

Please note:

• There are 4 modules in total.

• Labs are for verified users only. They are 'open book' and there is no set time limit. You will need a gmail account for the lab on Colab (Colabatory on Google) for Week 3.

• The exercises involve hands-on work with Python (we will provide useful hints)

• You will only have one attempt to answer each exercise.

• You can complete the exercises at any time while the course is open, however, we do recommend that you complete them sequentially, after you complete the relevant module.