Sometimes data science projects go awry, when the predictions made by statistical and AI / machine learning algorithms turn out to be not just wrong, but biased and unfair in ways that cause harm. This program, for both data science practitioners and managers, provides responsible guidance and practical tools to build better models and avoid these problems.
Overview
Syllabus
Courses under this program:
Course 1: Principles of Data Science Ethics
Course 2: Applied Data Science Ethics
Course 1: Principles of Data Science Ethics
Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.
This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.
Course 2: Applied Data Science Ethics
AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.
Courses
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Concern about the harmful effects of machine learning algorithms and 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, those who developed and deployed these algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.
This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies along with Python code are provided.
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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 ****
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Tools for model interpretability
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Global versus local model interpretability methods
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Metrics for model fairness
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Auditing your model for bias and fairness
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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.
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Taught by
Kuber Deokar, Janet Dobbins, Peter Bruce, Grant Fleming and Veronica Carlan
