Ethical Issues in Data Science from Coursera

Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning.

This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals.

Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

What's inside

Syllabus

Ethical Foundations

This module begins with an introduction to the course including motivation for the topic, the course goals, what topics the course will cover, and what is expected of the students. It then reviews the three ethical frameworks that are most commonly applied to ethical discussions in data science and computing: Kantianism/deontology, virtue ethics, and utilitarianism. Case studies are used to illustrate the application and properties of these frameworks.

Internet, Privacy, and Security

This module begins with some background about the Internet, which is the foundation for most of the topics that we study in this course. It then discusses the two most basic ethical issues in using the internet, privacy and security, in the context of data science. It goes through a number of real case studies and examples for each to illustrate the diversity of issues.

Professional Ethics

This module provides insight into the ethical issues in the data science profession and workplace (as opposed to technical topics in data science). It starts with discussion of two highly relevant codes of professional ethics, from professional societies in statistics and in computing. It then looks at a variety of recent workplace ethics issues in tech companies. A key part of this module is interviewing a data science professional about ethical issues they have encountered in their career.

Algorithmic Bias

Algorithmic bias may be the topic that people associate most with ethical issues in data science. This module begins by providing some general background on algorithmic bias and considering varying views on the pros and cons of algorithmic vs. human decision making. It then reviews an illustrative set of examples of algorithmic bias related to gender and race, which is a particularly important class of instances of algorithmic bias. The final part of the module discusses what is perhaps the single most prominent and discussed instance of algorithmic decision making and bias, facial recognition.

Medical Applications and Implications

Data science is applied to a wide variety of important application areas, each with their own ethical issues. This module focuses on an application area that is both particularly important and leads to a rich set of ethical issues: medical applications. This includes looking at current issues involved with health databases and the uses of artificial intelligence in healthcare, and more futuristic issues, gene editing and neurological interventions. The module concludes with a crucial topic that every data science profession should consider: the implications of the fields of data science and computing on the future of human work.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops ethical frameworks for data science professionals, which are currently sparse in industry

Taught by a researcher with extensive experience in ethics and data

Provides a combination of theoretical and practical approaches to ethical issues in data science

Explores real-world case studies to illustrate ethical considerations in data science applications

Can be taken for academic credit as part of a Master's degree in Data Science, indicating a rigorous and comprehensive curriculum

Reviews summary

Ethics in data science

Learners largely praise this info-packed course on ethical issues in data science. They found the readings in this course to be especially helpful. Many thought that this course was more work than traditional, technical courses. Still, they appreciate the emphasis on ethical thinking. One small downside is that the course requires a significant amount of writing, and learners wish there was an option to just audit the material without having to submit assignments.

Excellent readings

Gets into ethical discussions in data science

"A course full of valuable information and beautiful skills"

"This is an awesome general overview on the ethical issues we are likely to run into as data scientits and researchers"

"But please calculate a lot of more time for reading and writing as stated."

Lots of assignments

More writing than a typical course

"I learned a lot about ethical issues and computer Science. Good lectures, good reading material, but a whole lot of writing"

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 Ethical Issues in Data Science with these activities:

Brush up on Applied Mathematics

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Deepen your understanding of mathematical principles before the course begins to ensure a strong foundation for ethical considerations in data science.

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Review linear algebra and calculus
Solve practice problems and complete exercises to test your understanding

Attend a Data Ethics Workshop

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Engage with experts and practitioners to gain insights into best practices and practical applications of data ethics in real-world scenarios.

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Research upcoming workshops on data ethics
Register and attend a workshop that aligns with your interests
Participate actively, ask questions, and take notes

Participate in a Study Group

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Engage with fellow students to discuss ethical considerations, share perspectives, and enhance your understanding of the course material.

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Form or join a study group with other students enrolled in the course
Set regular meeting times and establish a discussion agenda
Take turns presenting on ethical topics and facilitate discussions

Four other activities

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Solve Ethical Decision-Making Exercises

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Engage with interactive exercises and scenarios to develop your ability to identify and resolve ethical dilemmas in data science.

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Access online or textbook resources providing ethical decision-making exercises
Complete the exercises and reflect on the ethical implications of your choices
Compare your responses with others to gain diverse perspectives

Explore Online Courses on Data Science Ethics

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Expand your knowledge and gain additional perspectives on data science ethics by enrolling in reputable online courses offered by platforms like Coursera or edX.

Browse courses on Data Science Ethics

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Research and identify online courses specifically focused on data science ethics
Enroll in a course that aligns with your learning objectives
Complete the course modules and actively participate in discussions

Develop a Case Study Analysis

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Apply ethical frameworks and principles to a real-world data science case study to enhance your critical thinking and decision-making skills.

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Identify a relevant data science case study
Analyze the ethical implications of the case study
Develop recommendations and strategies for addressing the ethical issues identified
Present your analysis and recommendations in a written report or presentation format

Contribute to Open-Source Projects Related to Data Ethics

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Gain practical experience and contribute to the advancement of ethical data science by participating in open-source projects that address ethical concerns.

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Identify open-source projects focused on data ethics and responsible data practices
Review the project documentation and identify areas where you can contribute
Make code contributions, participate in discussions, and engage with the project community

Career center

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

Data Scientist

Data Scientists study large, complex data sets to extract actionable insights. Ethical considerations are central to their work because they must make decisions about how to collect, store, and analyze data. This course's coverage of the ethical frameworks--Kantianism/deontology, virtue ethics, and utilitarianism--is especially relevant to the data science profession.

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Data Engineer

Data Engineers design, build, and maintain data pipelines. Ethical considerations come into play when they determine the data's source, the methods of data collection, and the data storage location. This course's exploration of the ethical issues related to data science, such as privacy and security and algorithmic bias, is essential knowledge for those seeking to enter the field of Data Engineering.

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Data Analyst

Data Analysts interpret and communicate data for business purposes. They must have a strong understanding of the ethical implications of their work. This course's focus on ethical frameworks and case studies provides foundational knowledge for Data Analysts who wish to make data-driven decisions that align with ethical principles.

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Machine Learning Engineer

Machine Learning Engineers develop and deploy machine learning models. This course's examination of ethical considerations in artificial intelligence and machine learning, such as algorithmic bias, is critical to ensuring their work aligns with ethical principles. Additionally, discussions about the privacy and security of data, which are key concerns in machine learning, are highly relevant to the role of a Machine Learning Engineer.

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Risk Manager

Risk Managers identify, assess, and mitigate risks. This course's emphasis on ethical frameworks and decision-making can help Risk Managers consider the ethical implications of their decisions. Additionally, the module on professional ethics provides valuable insights into maintaining ethical standards in the workplace.

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Compliance Officer

Compliance Officers ensure that organizations adhere to laws and regulations. This course's examination of ethical frameworks and case studies will assist Compliance Officers in understanding the ethical implications of compliance decisions. The module on professional ethics provides additional guidance on maintaining ethical standards in the workplace.

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Statistician

Statisticians collect, analyze, and interpret data. This course's coverage of ethical frameworks and statistical methods can help Statisticians make ethical decisions about data collection and analysis. The module on medical applications and implications is particularly relevant for Statisticians working in the healthcare industry.

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Computer Scientist

Computer Scientists design, develop, and implement computer systems. This course's emphasis on ethical frameworks and professional ethics provides Computer Scientists with guidance on making ethical decisions in the design and development of technology. The module on algorithmic bias is particularly relevant to the use of algorithms in computing systems.

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Information Security Analyst

Information Security Analysts protect computer systems and networks from unauthorized access. This course's examination of privacy and security issues in data science is essential knowledge for Information Security Analysts seeking to safeguard sensitive information. The module on professional ethics provides valuable insights into maintaining ethical standards in the workplace.

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Business Analyst

Business Analysts help businesses make data-driven decisions. This course's coverage of ethical frameworks and case studies can assist Business Analysts in understanding the ethical implications of business decisions. The module on privacy and security issues may be relevant to Business Analysts working with sensitive business information.

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IT Auditor

IT Auditors evaluate the effectiveness of an organization's computer systems and controls. This course's focus on ethical frameworks and professional ethics provides IT Auditors with guidance on making ethical decisions in the audit process. The module on privacy and security issues is particularly relevant to IT Auditors working with sensitive data and systems.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course's emphasis on ethical frameworks and professional ethics provides Software Engineers with guidance on making ethical decisions in the design and development of software. The module on algorithmic bias is particularly relevant to the use of algorithms in software applications.

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UX Designer

UX Designers create user-centered designs for websites and applications. This course's examination of ethical issues related to internet use, privacy, and security provides UX Designers with insights into designing systems that are both user-friendly and ethical. The module on professional ethics provides valuable guidance on maintaining ethical standards in the workplace.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and implement AI systems. This course's emphasis on ethical frameworks and artificial intelligence will equip Artificial Intelligence Engineers with the knowledge and skills to make ethical decisions in the development and deployment of AI systems. Discussions about the privacy and security of data, which are key concerns in AI, are also highly relevant to this role.

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Data Architect

Data Architects design and manage data systems. This course's coverage of ethical frameworks and data science applications can assist Data Architects in understanding the ethical implications of data management decisions. The module on medical applications and implications is particularly relevant for Data Architects working with sensitive healthcare data.

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