Abstract
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.
The readings are listed below.
You can find an annotated version here.
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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.
Books
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.
Articles
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. “12 Things Everyone Should Understand About Tech.” 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.