About this course

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.

At a Glance:
Institution: Statistics.comX
Subject: Ethics
Level: Intermediate
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)
Associated programs:
Professional Certificate in Data Science Ethics
Language: English
Video Transcript: English
Associated skills:Machine Learning, Data Ethics, Data Science, Artificial Intelligence, News Stories, Decision Making, Algorithms, Machine Learning Algorithms, Python (Programming Language), Auditing, Big Data, Data Analysis

What You’ll Learn:
About this course

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.