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Ethical and Inclusive Data Science Readings.pdf

Readings in Ethical Data Science

An Incomplete Reading List

The readings are listed below.

You can find an annotated version here.

Additions are welcome! To suggest an addition please file an Issue.

This reading list gives an overview of the ethical concerns specific to data analysis, data science, and artificial intelligence. Ethics is used broadly here to mean concerns related to racial and economic equity, justice, fairness, and the protection of democratic and human rights.

This list is intended to spark new ideas and prompt critical thinking about data system design and integration into business processes in an organization. This is not an endorsement of all viewpoints represented in the readings below – except to say that each of the readings raise questions, put forward ideas, and make critiques that are worthy of your deep consideration. All links last accessed July 11th, 2019.

This guide was last updated July 11th, 2019.


Great Overviews

Eubanks, Virginia. 2018. Automating Inequality. St. Martin’s Press.

Noble, Safiya. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.

Deep Dives

Broussard, Meredith. 2018. Artificial Unintelligence: How Computers Misunderstand the World. MIT Press.

Benjamin, Ruha. 2019. Race After Technology: Abolitionist Tools for the New Jim Code. Polity.

Practical Ways Forward

Loukides, Mike, Hilary Mason, and DJ Patil. 2018. Ethics and Data Science. O’Reilly.

Gathering Voices

brown, adrienne maree. 2017. Emergent Strategy: Shaping Change, Changing Worlds. AK Press.


Great Overviews

Wallach, Hanna. 2014. “Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency.” Medium. Online. 12.19.2014

O’Neil, Cathy. 2016. “How to Bring Better Ethics to Data Science.” Slate. Online. 2.4.2016

Broussard, Meredith. 2019. “Letting Go of Technochauvinism.” in Public Books. Online. 6.17.2019.

Government and Accountability

Fischer, Frank. 1993. “Citizen participation and democratization of policy expertise: From theoretical inquiry to practical cases.” Policy Sciences. v. 26 pp. 165-187.

Diakopoulos, Nicholas. 2016. “How to Hold Governments Accountable for the Algorithms They Use.” Slate. Online. 2.11.2016

Angwin, Julia. 2016. “Making Algorithms Accountable.” ProPublica. Online. 2.1.2016

Ethical Codes

Patil, DJ. 2016. “A Code of Ethics for Data Science.” Medium. Online. 2.1.2018

Wheeler, Schaun. 2018. “An ethical code can’t be about ethics.” Towards Data Science. Online. 2.6.2018

Eubanks, Virginia. 2018. “A Hippocratic Oath for Data Science.” Online. 2.21.2018

Technology and Our Lives

Dash, Anil. 2018. Humane Tech. Medium. Online.

Further Reading Lists

Venkatasubramanian, Suresh and Katie Shelef. 2017. “Ethics of Data Science Course Syllabus.” University of Utah. Online.

Malliaraki, Eirini. 2018. “Toward ethical, transparent and fair AI/ML: a critical reading list.” Medium. Online.

Wickham, Hadley. 2018. “Readings in Applied Data Science.” Online.

Various. 2018. Readings in Data Ethics. O’Reilly. Online.

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