Data Science Ethics from Coursera

What's inside

Syllabus

What are Ethics?

Module 1 of this course establishes a basic foundation in the notion of simple utilitarian ethics we use for this course. The lecture material and the quiz questions are designed to get most people to come to an agreement about right and wrong, using the utilitarian framework taught here. If you bring your own moral sense to bear, or think hard about possible counter-arguments, it is likely that you can arrive at a different conclusion. But that discussion is not what this course is about. So resist that temptation, so that we can jointly lay a common foundation for the rest of this course.

History, Concept of Informed Consent

Early experiments on human subjects were by scientists intent on advancing medicine, to the benefit of all humanity, disregard for welfare of individual human subjects. Often these were performed by white scientists, on black subject. In this module we will talk about the laws that govern the Principle of Informed Consent. We will also discuss why informed consent doesn’t work well for retrospective studies, or for the customers of electronic businesses.

Data Ownership

Who owns data about you? We'll explore that question in this module. A few examples of personal data include copyrights for biographies; ownership of photos posted online, Yelp, Trip Advisor, public data capture, and data sale. We'll also explore the limits on recording and use of data.

Privacy

Privacy is a basic human need. Privacy means the ability to control information about yourself, not necessarily the ability to hide things. We have seen the rise different value systems with regards to privacy. Kids today are more likely to share personal information on social media, for example. So while values are changing, this doesn’t remove the fundamental need to be able to control personal information. In this module we'll examine the relationship between the services we are provided and the data we provide in exchange: for example, the location for a cell phone. We'll also compare and contrast "data" against "metadata".

Anonymity

Certain transactions can be performed anonymously. But many cannot, including where there is physical delivery of product. Two examples related to anonymous transactions we'll look at are "block chains" and "bitcoin". We'll also look at some of the drawbacks that come with anonymity.

Data Validity

Data validity is not a new concern. All too often, we see the inappropriate use of Data Science methods leading to erroneous conclusions. This module points out common errors, in language suited for a student with limited exposure to statistics. We'll focus on the notion of representative sample: opinionated customers, for example, are not necessarily representative of all customers.

Algorithmic Fairness

What could be fairer than a data-driven analysis? Surely the dumb computer cannot harbor prejudice or stereotypes. While indeed the analysis technique may be completely neutral, given the assumptions, the model, the training data, and so forth, all of these boundary conditions are set by humans, who may reflect their biases in the analysis result, possibly without even intending to do so. Only recently have people begun to think about how algorithmic decisions can be unfair. Consider this article, published in the New York Times. This module discusses this cutting edge issue.

Societal Consequences

In Module 8, we consider societal consequences of Data Science that we should be concerned about even if there are no issues with fairness, validity, anonymity, privacy, ownership or human subjects research. These “systemic” concerns are often the hardest to address, yet just as important as other issues discussed before. For example, we consider ossification, or the tendency of algorithmic methods to learn and codify the current state of the world and thereby make it harder to change. Information asymmetry has long been exploited for the advantage of some, to the disadvantage of others. Information technology makes spread of information easier, and hence generally decreases asymmetry. However, Big Data sets and sophisticated analyses increase asymmetry in favor of those with ability to acquire/access.

Code of Ethics

Finally, in Module 9, we tie all the issues we have considered together into a simple, two-point code of ethics for the practitioner.

Attributions

This module contains lists of attributions for the external audio-visual resources used throughout the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for data analysts with limited prior knowledge of ethics

Delves into complex topics like algorithmic fairness and societal consequences of data science

Practical examples and case studies illustrate ethical dilemmas in data management

Covers foundational concepts in ethics, such as informed consent and privacy principles

Emphasizes the importance of shared ethical values in data science practices

Provides a framework for analyzing ethical concerns related to data privacy and control

Reviews summary

Data science ethics exploration

Learners say this largely positive course on data science ethics is engaging, well-paced, and thought-provoking. Students say the case studies and examples helped them understand the implications of data science ethics in the real world. Learners say the course is accessible even to those without a background in data science.

The course is accessible to those without a background in data science.

"Great overview of the topic with lots of case studies and examples. Would be accessible even to people with a limited data background"

"This is a good course that provides a high-level overview of different aspects of data ethics."

"The materials of this course is really good."

The course is concise but provides a comprehensive overview of ethical issues in data science.

"Not the longest course but it is concise enough and give you a very good overall understanding of the ethical situations in the current world."

"This course is really amazing for the data science professionals."

"A comprehensive course, with many real examples on the topic."

Actionable steps for promoting ethical practices in data science are provided.

"The course provided practical tools and frameworks for identifying and mitigating ethical risks, which I found very useful."

"I will apply the things I have learned here on my job as a Safety personnel."

The course provides real-world examples and case studies to illustrate ethical concepts.

"Course was filled with great examples and lots of videos to stay engaged with the content. Highly recommend."

"Amazing course that is being carefully prepared. The case studies are classic hence it is rather easy to correlate with the identified problem being introduced throughout the course."

"I recently completed the Data Science Ethics course offered by the University of Michigan through Coursera, and I must say it was an eye-opening experience."

Widely considered as an excellent course.

"One of the most engaging course on Coursera !! Well done !!"

"Excellent course. There are issues in DS Ethics, especially the Face Recognition aspects."

"An excellent grounding in the ethical considerations in the field of data science."

The instructor is knowledgeable, engaging, and clear.

"Great instructor. Very well balanced approach to what could easily turn into a political or ideologically biased topic."

"The instructor really explained everything well and in detailed manner."

"The instructor made the course interesting all through out."

Certificate difficulties with peer review.

"This course asks true or false questions when the answer can be either yes or no, or true or false, depending upon circumstances, and where the circumstances aren't specified in the questions - which present only absolute/binary answer options with no opportunity to clarify or expound."

"I haven't got my certificate yet so please give me my certificate this is all i wanted to say"

"The course content is actually quite good and thought provoking, but the final submission requires peer review which appears to be practically impossible to get."

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 Data Science Ethics with these activities:

Review basic concepts of statistics and probability

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Ensure a strong foundation in statistics and probability for understanding data analysis techniques.

Browse courses on Statistics

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Revisit textbooks or online resources to refresh your understanding of basic statistical concepts
Practice solving probability problems to enhance your analytical skills

Review the book 'Ethics of Big Data' by Viktor Mayer-Schönberger

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Gain a comprehensive understanding of the ethical implications of big data from a leading expert.

View Access Rules on Amazon

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Read the book thoroughly and take notes on key concepts and arguments
Identify the main ethical challenges discussed in the book
Summarize the author's proposed solutions and evaluate their strengths and weaknesses

Practice exercises on data privacy and ethics

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Reinforce understanding of data privacy and ethical considerations through repetitive exercises.

Browse courses on Data Privacy

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Complete the quizzes at the end of each module to test your understanding
Participate in the discussion forums to engage with others and discuss ethical scenarios

Five other activities

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Show all eight activities

Participate in peer discussions on data ethics cases

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Gain diverse perspectives and enhance critical thinking skills by engaging in peer discussions.

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Join or create study groups with classmates to discuss course materials
Engage in online forums or discussion boards to share insights and learn from others

Follow tutorials on data ethics and privacy best practices

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Enhance knowledge of ethical data practices by following guided tutorials from experts.

Browse courses on Data Privacy

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Explore online resources and videos to gain additional insights into data privacy and ethics
Attend webinars or online workshops on best practices for handling sensitive data

Attend a workshop on responsible data management

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Expand knowledge and skills in responsible data management through hands-on learning.

Browse courses on Data Privacy

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Identify and attend workshops or seminars on data ethics and privacy best practices
Participate actively in discussions and ask questions to gain insights from experts in the field

Create a presentation on the ethical implications of big data

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Deepen understanding of the ethical considerations in big data by creating a presentation.

Browse courses on Big Data

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Research and gather information on the ethical implications of big data collection and use
Develop a compelling narrative that highlights the key ethical issues and their potential impact
Design engaging slides and visuals to support your presentation
Practice presenting your ideas clearly and effectively

Develop a data ethics policy for a hypothetical organization

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Apply ethical principles to real-world scenarios by creating a comprehensive data ethics policy.

Browse courses on Data Ethics

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Research and analyze existing data ethics policies and frameworks
Identify the specific ethical considerations relevant to the hypothetical organization
Develop a clear and concise policy that addresses the identified ethical issues
Include mechanisms for monitoring and enforcing the policy

Career center

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

Data Ethics Officer

Data Ethics Officers are responsible for ensuring that their company's use of data is ethical and responsible. This course would provide a comprehensive overview of data ethics, including topics such as privacy, algorithmic fairness, and societal consequences of data science. This knowledge would be essential for Data Ethics Officers who want to ensure that their company's use of data is ethical and responsible.

See salaries and explore the career path for Data Ethics Officer

Data Governance Specialist

Data Governance Specialists are responsible for developing and implementing policies and procedures to ensure that their company's data is used ethically and responsibly. This course would provide a strong foundation in data ethics, which is essential for Data Governance Specialists. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Data Governance Specialist

Privacy Officer

Privacy Officers are responsible for protecting their company's data from unauthorized access and use. This course would provide a strong foundation in data ethics, which is essential for Privacy Officers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Privacy Officer

Data Scientist

Data Scientists analyze data to extract meaningful insights that can be used to make better decisions. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science. This knowledge would be invaluable to Data Scientists who want to ensure that their work is ethical and responsible.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to identify trends and patterns that can help businesses make better decisions. This course would provide a solid foundation in data ethics, which is becoming increasingly important as businesses collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science. This knowledge would be invaluable to Data Analysts who want to ensure that their work is ethical and responsible.

See salaries and explore the career path for Data Analyst

Information Security Analyst

Information Security Analysts are responsible for protecting their company's data from unauthorized access and use. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Information Security Analyst

Risk Analyst

Risk Analysts are responsible for identifying and assessing risks to their company's data. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Risk Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses understand their customers and make better decisions. This course would provide a strong foundation in data ethics, which is becoming increasingly important as businesses collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science. This knowledge would be invaluable to Business Intelligence Analysts who want to ensure that their work is ethical and responsible.

See salaries and explore the career path for Business Intelligence Analyst

Compliance Officer

Compliance Officers are responsible for ensuring that their company complies with applicable laws and regulations. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Compliance Officer

Financial Analyst

Financial Analysts are responsible for analyzing financial data to make investment recommendations. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Financial Analyst

Auditor

Auditors are responsible for reviewing and evaluating their company's financial and operational data. This course would provide a strong foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Auditor

Investment Banker

Investment Bankers advise companies on mergers and acquisitions, capital raising, and other financial transactions. This course would provide a solid foundation in data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

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Management Consultant

Management Consultants advise companies on a wide range of business issues, including strategy, operations, and technology. This course would provide a broad overview of data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Management Consultant

Product Manager

Product Managers are responsible for developing and managing products. This course would provide a basic understanding of data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Product Manager

Software Engineer

Software Engineers design, develop, and maintain software applications. This course would provide a basic understanding of data ethics, which is becoming increasingly important as companies collect more data on their customers. The course covers topics such as privacy, algorithmic fairness, and societal consequences of data science.

See salaries and explore the career path for Software Engineer