Applied Data Science Ethics

Concern about the harmful effects of machine learning algorithms and big data AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, the data scientists who developed and deployed these decision making algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.

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.

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.