Principles of Data Science Ethics from edX

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.

What you'll learn

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

What's inside

Learning objectives

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

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

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

Legal Issues EU

Existing laws

Reading / Discussion Prompt 2

Exercise 3 (for verified users only)

Week 3 – Transparency

Model interpretability

Global interpretability methods

Reading

Exercise 4 & 5 (for verified users only)

Week 4 – Principles and Frameworks

Introduction to Principles of Responsible Data Science (RDS)

From Principles to Practice

RDS Framework

Return to CRISP-DM

Exercise 6 (for verified users only)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches Responsible Data Science framework, which is important for increasing awareness of data ethics

Provides guidance and practical tools to help build better models and avoid harmful impacts of AI models

Covers legal issues related to data ethics, ensuring learners are aware of potential legal implications

Emphasizes interpretability of models, which is essential for avoiding harm and ensuring responsible use of AI

Taught by recognized instructors in the field, bringing expertise and credibility to the course

Provides case studies and Python code for practical application and reinforcement of concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Principles of Data Science Ethics with these activities:

Review regression & classification techniques

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Refresh your knowledge of regression and classification before taking the course to ensure a solid foundation.

Browse courses on Regression

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Read lecture notes on regression and classification techniques
Complete practice problems on regression and classification algorithms
Review examples of regression and classification models in real-world applications

Review CRISP-DM framework

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Review the key concepts of the CRISP-DM framework to enhance your understanding of the data science process and prepare for the course materials.

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Read the CRISP-DM white paper
Watch a video tutorial on the CRISP-DM framework
Summarize the key steps and phases of the CRISP-DM framework

Build a small machine learning model

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Putting machine learning concepts into practice by building your own model will strengthen your understanding.

Browse courses on Modeling

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Choose a simple dataset to work with
Select appropriate machine learning algorithms for the task
Train and evaluate the model using Python or other tools

One other activity

Expand to see all activities and additional details

Show all four activities

Solve practice problems on bias mitigation

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Engage in practice problems that simulate real-world scenarios to reinforce your understanding of bias mitigation techniques and their applications.

Browse courses on Bias Mitigation

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Attempt a Kaggle competition on bias mitigation
Work through a set of practice problems on fairness metrics
Participate in a peer group to discuss and analyze bias mitigation strategies

Career center

Learners who complete Principles of Data Science Ethics will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists use machine learning and other techniques to extract insights from data. This course teaches the responsible development and deployment of AI models, which is crucial for data scientists who want to avoid the unintended harmful effects of their work. The course also provides case studies and Python code, making it a practical resource for data scientists of all levels.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers build and maintain machine learning models. This course teaches the fundamentals of data ethics, which is essential for machine learning engineers who want to create models that are fair and unbiased. The course also provides guidance on how to evaluate the tradeoffs between model performance and interpretability.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts use data to solve business problems. This course teaches the importance of interpretability in avoiding harm, which is critical for data analysts who want to make sure that their insights are clear and actionable. The course also provides a framework for evaluating the ethical implications of data analysis projects.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians use statistical methods to analyze data. This course teaches the principles of responsible data science, which is essential for statisticians who want to ensure that their work is ethical and unbiased. The course also provides guidance on how to communicate statistical findings in a clear and concise manner.

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Business Analyst

Business analysts use data to make business decisions. This course teaches the importance of considering the ethical implications of data analysis, which is critical for business analysts who want to make decisions that are fair and equitable. The course also provides a framework for developing data-driven solutions that are aligned with organizational values.

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Data Engineer

Data engineers build and maintain data pipelines. This course teaches the importance of data quality and data governance, which is essential for data engineers who want to ensure that their work is ethical and compliant. The course also provides guidance on how to design and implement data pipelines that are secure and reliable.

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Risk Manager

Risk managers identify and mitigate risks. This course teaches the importance of considering the ethical implications of risk management decisions, which is critical for risk managers who want to make decisions that are fair and equitable. The course also provides a framework for developing risk management strategies that are aligned with organizational values.

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Compliance Officer

Compliance officers ensure that organizations comply with laws and regulations. This course teaches the importance of understanding the ethical implications of compliance decisions, which is critical for compliance officers who want to make decisions that are fair and equitable. The course also provides a framework for developing compliance programs that are aligned with organizational values.

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Auditor

Auditors evaluate the financial statements of organizations. This course teaches the importance of considering the ethical implications of auditing decisions, which is critical for auditors who want to make decisions that are fair and equitable. The course also provides a framework for developing audit procedures that are aligned with organizational values.

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Consultant

Consultants provide advice to businesses and organizations. This course teaches the importance of considering the ethical implications of consulting decisions, which is critical for consultants who want to make decisions that are fair and equitable. The course also provides a framework for developing consulting recommendations that are aligned with client values.

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Project Manager

Project managers plan and execute projects. This course teaches the importance of considering the ethical implications of project management decisions, which is critical for project managers who want to make decisions that are fair and equitable. The course also provides a framework for developing project plans that are aligned with organizational values.

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Product Manager

Product managers develop and launch products. This course teaches the importance of considering the ethical implications of product development decisions, which is critical for product managers who want to make decisions that are fair and equitable. The course also provides a framework for developing product roadmaps that are aligned with customer values.

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Sales Manager

Sales managers lead sales teams. This course teaches the importance of considering the ethical implications of sales decisions, which is critical for sales managers who want to make decisions that are fair and equitable. The course also provides a framework for developing sales strategies that are aligned with company values.

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Marketing Manager

Marketing managers develop and execute marketing campaigns. This course teaches the importance of considering the ethical implications of marketing decisions, which is critical for marketing managers who want to make decisions that are fair and equitable. The course also provides a framework for developing marketing campaigns that are aligned with company values.

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Human Resources Manager

Human resources managers oversee the human resources department. This course may be useful for human resources managers who want to learn more about the ethical implications of human resources decisions. The course provides a framework for developing human resources policies that are aligned with company values.

See salaries and explore the career path for Human Resources Manager