Ethics and Data Science

by Mike Loukides, Hilary Mason, DJ Patil

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors examine practical ways for making ethical data standards part of your work every day.

Genres: Textbooks, Philosophy, Technology

46 pages, Kindle Edition

Published July 25, 2018

Reviews

[Michael Burnam-Fink · Rating 4 out of 5]

Ethics and Data Science has two important virtues of being free and short, which make it a decent starting place for a conversation about ethics and data science. However, it doesn't do much to advance the conversation beyond hoary tropes to "do better" with caring for user data.

The basic premise is that programming ethics is more than a code or an oath, it's a daily practice that can made explicit by checklists to question the assumptions going into your program, and "five Cs" to follow, in treating customer data as your own personal data.

Getting ethics right is important. Facebook's Cambridge Analytica related scandals are only the tip of the data iceberg. But I'm not sure that 20th century ideals of informed consent have much to say about the sheer combinatorial velocity of data in the 21st century.

[Jindřich Mynarz · Rating 3 out of 5]

It's good. Short, written in clear language, and free. A book on data ethics for practical people.

[jasmine sun · Rating 4 out of 5]

practical, short (<30 min), free.

primer on building ethical data products - not at all nuanced, but great starting point for students about to begin their first project (especially if in a sensitive domain). goes over concepts like DJ Patil's 5 Cs, basic case studies, and building ethics into process/culture.

the links were some of the most helpful resources (especially the UK's data ethics framework and workbook: https://www.gov.uk/government/publica...).

likely will incorporate into curriculum for a project-based public interest tech course at stanford.

[Frank · Rating 5 out of 5]

Every data scientist or software engineer working with Big Data should read this book! It's short and to the point, distilling what could be a confusing consideration of ethics into a few simple approaches, which will make a big difference for any applied project. Starting with the obvious, pledges and creeds, it quickly shows why they're inadequate. Then, the authors discuss the checklist approach, which is applied, systematic, and reproducible. The book gives practical examples of ethical challenges, showing how easily unexamined projects with noble intentions can go awry! Just raising awareness is a big take-away from the book - and if teams develop and apply the checklist approach, it's far more likely they'll deliver solutions that actually "Walk the Talk" of consumer privacy and responsible data management.

[Tomiwa · Rating 4 out of 5]

A good read and somewhat practical approach on data ethics. It strongly denounces current industry practices. It provides guidelines and approaches to managing data ethics and corporate responsibilities.
Albeit some of its methodologies are not feasible. For instance, it suggests enforcing data ethics and principles on students' academic submissions, MVP projects and online courses. Surely, a proper consideration and implementation will be at the expense of additional time, money, efforts and resources.

[Karthikeya Racharla · Rating 5 out of 5]

This short read weaves through the perspectives of guidelines, checklists and 5C Terminology, while dealing with sensitive information. Many instances of data being used unethical were quoted and possible remedies were also suggested. At the end, this book briefs exemplary real-world cases by Princeton Researchers. Different practises like Lean Methodologies, Bug bounties etc were also focused upon.

Overall, very good book for Data-driven Product, Strategy industry folks to ponder upon.

[Astrid Paramita · Rating 5 out of 5]

Simple and practical guide to implement data science ethics. Along with case studies and explanations of why such thing is very important to implement in a company. A book that needs to be picked up, read, and re-read for data scientists, engineers, and decision makers in (especially) a data driven company.

[Magdalena · Rating 4 out of 5]

Concise

Well, given its only 40 pages long this book is short but covers the key principles of data ethics - 5Cs. Few good links to sources out there and thought provoking content. Don't think you will discover anything new if you know something about data privacy and ethical use of data - but it's a good reminder that you need to live ethics as there is no easy fix.

[Kyle Hayes · Rating 4 out of 5]

Nice thought primer

A brief introduction to get us thinking about important ethics concepts in data science. The book does a great job of covering a lot of ground in a concise format.

[Felipe · Rating 3 out of 5]

A small and quick book with good points about the importance and necessity on considering ethics when working with data Science. A good point about this book is that it bring no only tough about it but also propose on how to consider ethical issues.

[Yuvi Panda · Rating 3 out of 5]

Short and useful

Very short book. Wonderfuly simple framework and lots of pointers to other, more substantial books. Great starter book for newbies

[CBru1011 · Rating 5 out of 5]

Helpful Primer

I was interested in learning more about Ethics in Data Science and Machine Learning. This book "got my feet wet" and pointed me toward more helpful resources.

[Patrick Millais · Rating 4 out of 5]

A neat little primer on engraining data ethics into product/process culture with some useful pointers towards further resources for deeper learning.

[Aria Neruda · Rating 5 out of 5]

A must-need, well-written book for professional and soon-to-be data scientists in developing an ethical data-driven product. To top it all, it's concise and free.

[Shawn · Rating 3 out of 5]

Decent overview. Useful mostly for links to more thorough sources. Nothing technically useful.

[Edkallenn Lima · Rating 4 out of 5]

Very good reading. An important debate about ethics and data science. It talks brightly about current issues in that topic. I recommend.

[Chalse · Rating 5 out of 5]

Short and to the point, good for a textbook

[Srikar · Rating 4 out of 5]

A short and succinct read on the need and importance of Ethics in the ever booming field.