Google Drive link to resource summaries
DATA SCIENCE CODE OF PROFESSIONAL CONDUCT, Prepared by the Data Science Association
Data Science Ethical Framework
Derman, E. Wilmott, P. (2009). The financial modeler’s manifesto
Taleb, N., Sandis, C. (2013). The skin in the game heuristic for protection against tail events
Ethical OS Toolkit - Anticipating the future impact of today's technology
When Data Science Destabilizes Democracy and Facilitates Genocide - by Rachel Thomas
The Public Voice Coalition - Universal Guidelines for Artificial Intelligence - These guidelines, endorsed by Sir Tim Berners-Lee, cover basic ethical principles that should guide all AI development. They list 12 principles that all AI development should answer to in order to "maximize the benefits of AI, to minimize the risk, and to ensure the protection of human rights."
Monmonier, M. (2005). Lying with Maps. Statistical Science, 20(3), 215-222. pdf book review
Ananny, M. (2016). Toward an Ethics of Algorithms. Science, Technology & Human Values, 41(1), 93-117. doi:10.1177/0162243915606523
Mutlu, C. E. (2015). Of Algorithms, Data and Ethics: A Response to Andrew Bennett1. Millennium (03058298), 43(3), 998-1002. doi:10.1177/0305829815581536
Ziewitz, M. (2016). Governing Algorithms. Science, Technology & Human Values, 41(1), 3. doi:10.1177/0162243915608948
Diakopoulos, N. (2016). Accountability in Algorithmic Decision Making. Communications Of The ACM, 59(2), 56-62. doi:10.1145/2844110
Mikton, J. (2015). The Internet of Things: ethics of our connectivity. International Schools Journal, 35(1), 56.
Tewell, E. e. (2016). Toward the Resistant Reading of Information: Google, Resistant Spectatorship, and Critical Information Literacy. Portal: Libraries & The Academy, 16(2), 289-310.
Kraemer, F., van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms?. Ethics & Information Technology, 13(3), 251. doi:10.1007/s10676-010-9233-7
Neyland, D. (2016). Bearing Account-able Witness to the Ethical Algorithmic System. Science, Technology & Human Values, 41(1), 50. doi:10.1177/0162243915598056
Raymond, A. H., & Shackelford, S. J. (2014). TECHNOLOGY, ETHICS, AND ACCESS TO JUSTICE: SHOULD AN ALGORITHM BE DECIDING YOUR CASE?. Michigan Journal Of International Law, 35(3), 485-524.
Moreau, N. (2008). Is It Ethical for Patents to Be Issued for the Computer Algorithms that Affect Course Management Systems for Distance Learning?. American Journal Of Distance Education, 22(4), 187-194.
Dobson, J. E. (2015). Can an algorithm be disturbed? Machine learning, intrinsic criticism, and the digital humanities. College Literature, (4), 543.
Grubaugh, C. (2014). The ethical obligations of a banal, content apocalypse. Kybernetes, 43(6), 947. doi:10.1108/K-05-2013-0098
Kijowski, D., Dankowicz, H., & Loui, M. (2013). Observations on the Responsible Development and Use of Computational Models and Simulations. Science & Engineering Ethics, 19(1), 63. doi:10.1007/s11948-011-9291-1
Al-Saggaf, Y., & Islam, M. Z. (2015). Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem. Science And Engineering Ethics, (4), 941. doi:10.1007/s11948-014-9564-6
Ethics & Data Science - Jeff Hammerbacher
Majken Sander and Joerg Blumtritt – algorithm ethics = value judgements
algorithmic hiring: Zeynep Tufekci
Understanding Bias in Machine Learning -- Jindong Gu and Daniela Oelke This article explores three ways bias can be introduced to ML algorithms from the perspective of an ML practitioner. Bias plays a crucial role in influencing algorithmic decision-making (e.g. due to an imbalanced dataset, the algorithm can start to form racist stereotypes), which makes it an important topic to consider for the ethics of data science.
Schrock, A. R. (2016) Civic hacking as data activism and advocacy: A history from publicity to open government data. New Media & Society, 18, 581-599.
-
A Reactive Approach for Use-Based Privacy, Eleanor Birrell Fred B. Schneider, 2017
-
Riazi, S. M., B. D. Rouhani, and F. Koushanfar, "Deep Learning on Private Data", to appear in IEEE Security and Privacy Magazine, 03/2018. The advent of machine learning as a service demands the need for privacy; this article discusses methods for privacy-preserving deep learning and inference.
- Council for Big Data, Ethics, and Society
- The Ethics of Using Hacked Data:Patreon’s Data Hack and Academic Data Standards
- A YouTube Engineer's Decision to Alter Data in the 'It Gets Better Project
- Trust and the Collection, Selection, Analysis and Interpretation of Data: A Scientist's View
- Developing ethical and privacy sensitivity towards geocoded data