Course description
Applied Data Science Ethics
This data science ethics course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models, do better data analysis and avoid these problems. You'll learn about
- Tools for model interpretability
- Global versus local model interpretability methods
- Metrics for model fairness
- Auditing your model for bias and fairness
- Remedies for biased models
The course offers real world problems and datasets, a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies with ethical considerations, along with Python code, are provided.
Who should attend?
Prerequisites:
- Principles of Data Science Ethics
- We will present Python code to illustrate, so we assume some familiarity with Python.
- You will need a gmail account for the lab in Module 3 which is housed at Colab (Colaboratory by Google)
Training content
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)
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)
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)
Tools and Documentation
- Videos:
- Tools and Documentation
- Readings
- Knowledge Checks
- Quiz (for verified users only)
Course delivery details
This course is offered through Statistics.com, a partner institute of EdX.
4-5 hours per week
Costs
- Verified Track -$198.97
- Audit Track - Free
Certification / Credits
What you'll learn
- 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.
About edX
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