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Statistics.comX: 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.

Principles of Data Science Ethics
4 weeks
4–5 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

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Starts Nov 14
Ends Dec 31
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Principles of Data Science Ethics

About this course

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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.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated programs:
  • Associated skills:Data Science, Data Ethics, Algorithms, Auditing, Artificial Intelligence, Python (Programming Language), News Stories, Machine Learning Algorithms

What you'll learn

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After completing this course you should be able to:

  • Identify and anticipate the types of unintended harm that can arise from AI models
  • Explain why interpretability is key to avoiding harm
  • Distinguish between intrinsically interpretable models and black box models
  • Evaluate tradeoffs between model performance and interpretability
  • Establish a Responsible Data Science framework for your projects

Syllabus

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

Week 1 – Landscape of Harm

  • Videos:

    • AI and Big Brother

    • Unintended harm

    • Types of harm

    • Best Practices - CRISP-DM

    • A bit of ancient history (verified users only)

  • Knowledge Checks

  • Reading / Discussion Prompt 1

  • Exercise 1 & 2 (for verified users only)

Week 2 – Legal Issues

  • Videos:

    • Legal Issues EU

    • Existing laws

  • Knowledge Checks

  • Reading / Discussion Prompt 2

  • Exercise 3 (for verified users only)

Week 3 – Transparency

  • Videos:

    • Model interpretability

    • Global interpretability methods

  • Knowledge Checks

  • Reading

  • Exercise 4 & 5 (for verified users only)

Week 4 – Principles and Frameworks

  • Videos:

    • Introduction to Principles of Responsible Data Science (RDS)

    • From Principles to Practice

    • RDS Framework

    • Return to CRISP-DM

  • Knowledge Checks

  • Exercise 6 (for verified users only)

This course is part of Data Science Ethics Professional Certificate Program

Learn more 
Expert instruction
2 skill-building courses
Self-paced
Progress at your own speed
2 months
4 - 5 hours per week

Ways to take this course

Choose your path when you enroll.

Certificate

Free

Price

$249 USD

-

Access to course materials

Unlimited

Limited

Expires on Dec 12

World-class institutions and universities

edX support

Shareable certificate upon completion

Graded assignments and exams

Read our FAQsin a new tab about frequently asked questions on these tracks.

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