Ethics of CSS
"Computational social science (CSS) is a field that integrates the study of humans and groups at all scales through the formal methodology of computational and mathematical models. Its purpose is to advance scientific understanding of social phenomena using computation, both as a conceptual and theoretical paradigm, based on information processing, and as a methodological tool." - Claudio Cioffi (2021), Handbook of Computational Social Science
As enviced by the above definition, CSS is a field of growing significance both in human society and in terms of its impact on our individual lives. It's outcomes are likely to play an increasing role in shaping our world and its research methods present novel opportunities and challenges. In light of this, it is important that we consider the Ethics of CSS, the moral promise it presents and the moral perils it raises. This is part of the Master study course CSS. Collected here, and divided into pertinent categories, is a selection of literature and media tackling this topic from a host of perspectives for the benefit of both experts and interested laypeople.
A Collection of Resources
What is CSS?
- Oboler, A., Welsh, K., & Cruz, L. (2012). The danger of big data: Social media as computational social science. First Monday.
- Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and explanation in social systems. Science, 355(6324), 486-488.
- Rains, S. A. (2020). Big data, computational social science, and health communication: A review and agenda for advancing theory. Health communication, 35(1), 26-34.
- Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., ... & Helbing, D. (2012). Manifesto of computational social science. The European Physical Journal Special Topics, 214(1), 325-346.
- Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6-13.
- Alvarez, R. M. (Ed.). (2016). Computational social science. Cambridge University Press.
Introduction to Ethics in CSS
- Zimmer, M., & Kinder-Kurlanda, K. (2017). Internet research ethics for the social age: New challenges, cases, and contexts. Peter Lang International Academic Publishers.
- Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2, 13.
- Keegan, B. C., & Matias, J. N. (2015). Actually, it's about ethics in computational social science: A multi-party risk-benefit framework for online community research. arXiv preprint arXiv:1511.06578.
- Giglietto, F., & Rossi, L. (2012). Ethics and interdisciplinarity in computational social science. Methodological Innovations Online, 7(1), 25-36.
Algorithms in Society
- Diallo, S. Y., Shults, F. L., & Wildman, W. J. (2021). Minding morality: ethical artificial societies for public policy modeling. Ai & Society, 36(1), 49-57.
- Baum, S. D. (2020). Social choice ethics in artificial intelligence. AI & SOCIETY, 35(1), 165-176.
- Greco, G. M., & Floridi, L. (2004). The tragedy of the digital commons. Ethics and Information Technology, 6(2), 73-81.
- Olhede, S. C., & Wolfe, P. J. (2018). The growing ubiquity of algorithms in society: implications, impacts and innovations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170364.
- Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170357.
- Ekström, A. (2015). The moral bias behind your search results. TED Talk.
- Harris, T. (2017). How a handful of tech companies control billions of minds every day. TED Talk.
- Zuckerman, E. (2014). The Internet’s original sin. The Atlantic, 14(08).
Ethics in Algorithmic Decision-making
- Segun, S. T. (2021). From machine ethics to computational ethics. AI & SOCIETY, 36(1), 263-276.
- Piano, S. L. (2020). Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities and Social Sciences Communications, 7(1), 1-7.
- Lepri, B., Oliver, N., & Pentland, A. (2021). Ethical machines: the human-centric use of artificial intelligence. Iscience, 102249.
- Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
Information Ethics
Privacy
Ethics of Current Tech in CSS
- Collection of articles on Descriptive Ethics for COVID19 Apps
- Blog: Philosophers On GPT-3
- Poursabzi-Sangdeh, F., Samadi, S., Vaughan, J. W., & Wallach, H. (2020). A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition. In CHI Workshop on Human-Centered Approaches to Fair and Responsible AI.
Ethics of Social Media Research
- Zimmer, M. (2010). “But the data is already public”: on the ethics of research in Facebook. Ethics and information technology, 12(4), 313-325.
- Mikal, J., Hurst, S., & Conway, M. (2016). Ethical issues in using Twitter for population-level depression monitoring: a qualitative study. BMC medical ethics, 17(1), 1-11.
- Locatelli, E. (2020). Ethics of Social Media Research: State of the Debate and Future Challenges. Second International Handbook of Internet Research, 835-856.
- Golder, S., Ahmed, S., Norman, G., & Booth, A. (2017). Attitudes toward the ethics of research using social media: a systematic review. Journal of medical internet research, 19(6), e7082.
- Boyd, D. (2016). Untangling research and practice: What Facebook’s “emotional contagion” study teaches us. Research Ethics, 12(1), 4-13.
- Golder, S., Ahmed, S., Norman, G., & Booth, A. (2017). Attitudes toward the ethics of research using social media: a systematic review. Journal of medical internet research, 19(6), e7082.
- Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61 million-person experiment in social influence and political mobilization. Nature, 489(7415), 295-298.
Science in the Era of Big Data
- Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
- Boyd, D., & Crawford, K. (2011, September). Six provocations for big data. In A decade in internet time: Symposium on the dynamics of the internet and society.
- Goldin, I. (2010). World wide research: Reshaping the sciences and humanities. MIT Press.
- Poor, N., & Davidson, R. (2017). The ethics of using hacked data: Patreon’s data hack and academic data standards. Internet Research Ethics for the Social Age, 278-280.
- Ellis, J. T. (2013). Undoing Ethics: Rethinking Practice in Online Research. Ethical Human Psychology and Psychiatry, 15(3), 209.
- Zook, M., Barocas, S., Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P., ... & Pasquale, F. (2017). Ten simple rules for responsible big data research.
- Fiske, S. T., & Hauser, R. M. (2014). Protecting human research participants in the age of big data.
- O'Neil, C. (2017). The Era of Blind Faith in Big Data Must End. Ted Talks.
- Sætra, H. S. (2018). Science as a vocation in the era of big data: The philosophy of science behind big data and humanity’s continued part in science. Integrative Psychological and Behavioral Science, 52(4), 508-522.
Research Ethics in CSS
- Jouhki, J., Lauk, E., Penttinen, M., Sormanen, N., & Uskali, T. (2016). Facebook’s emotional contagion experiment as a challenge to research ethics. Media and Communication, 4.
- Hutton, L., & Henderson, T. (2015, April). " i didn't sign up for this!": Informed consent in social network research. In Ninth International AAAI Conference on Web and Social Media.
- Beaulieu, A., & Estalella, A. (2012). Rethinking research ethics for mediated settings. Information, Communication & Society, 15(1), 23-42.
- Giglietto, F., & Rossi, L. (2012). Ethics and interdisciplinarity in computational social science. Methodological Innovations Online, 7(1), 25-36.
- Keegan, B. C., & Matias, J. N. (2015). Actually, it's about ethics in computational social science: A multi-party risk-benefit framework for online community research. arXiv preprint arXiv:1511.06578.
- Video: When Big Data, Research Ethics, & Human Rights Collide!
- Metcalf, J., & Crawford, K. (2016). Where are human subjects in big data research? The emerging ethics divide. Big Data & Society, 3(1), 2053951716650211.
- Shilton, K., & Sayles, S. (2016, January). " We Aren't All Going to Be on the Same Page about Ethics": Ethical Practices and Challenges in Research on Digital and Social Media. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 1909-1918). IEEE.
- Lipworth, W., Mason, P. H., Kerridge, I., & Ioannidis, J. P. (2017). Ethics and epistemology in big data research. Journal of bioethical inquiry, 14(4), 489-500.
- Chiauzzi, E., & Wicks, P. (2019). Digital trespass: ethical and terms-of-use violations by researchers accessing data from an online patient community. Journal of Medical Internet Research, 21(2), e11985.
AI for Social Good
- Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771-1796.
- Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., ... & Clopath, C. (2020). AI for social good: unlocking the opportunity for positive impact. Nature Communications, 11(1), 1-6.
- Pariser, E. (2019). What obligation do social media platforms have to the greater good?. Ted Talk
Toward Ethical AI Applications
- Silva, R., & Iqbal, R. (2018). Ethical implications of social internet of vehicles systems. IEEE Internet of Things Journal, 6(1), 517-531.
- Leben, D. (2017). A Rawlsian algorithm for autonomous vehicles. Ethics and Information Technology, 19(2), 107-115.
- Luxton, D. D. (2014). Recommendations for the ethical use and design of artificial intelligent care providers. Artificial intelligence in medicine, 62(1), 1-10.
- Wangmo, T., Lipps, M., Kressig, R. W., & Ienca, M. (2019). Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders. BMC medical ethics, 20(1), 1-11.
- De Togni, G., Erikainen, S., Chan, S., & Cunningham-Burley, S. (2021). What makes AI ‘intelligent’and ‘caring’? Exploring affect and relationality across three sites of intelligence and care. Social Science & Medicine, 277, 113874.
- Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of medical Internet research, 21(5), e13216.
Future of Work
- Susskind, D. (2020). A world without work: Technology, automation and how we should respond. Penguin UK.
- Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926.
Technology and Happiness
Framework for Ethical Algorithms
- ACM FAcct Conference, 2021
- Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6), 3333-3361.
- Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020, April). Co-designing checklists to understand organizational challenges and opportunities around fairness in ai. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
- Video: Ethics and Diversity in AI
- Hamidi, F., Scheuerman, M. K., & Branham, S. M. (2018, April). Gender recognition or gender reductionism? The social implications of embedded gender recognition systems. In Proceedings of the 2018 chi conference on human factors in computing systems (pp. 1-13).
- Floridi, L., & Sanders, J. W. (2001). Artificial evil and the foundation of computer ethics. Ethics and Information Technology, 3(1), 55-66.
- Video: The Coded Gaze: Bias in Artificial Intelligence
- Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
- Kasirzadeh, A. (2021). Reasons, values, stakeholders: a philosophical framework for explainable artificial intelligence. arXiv preprint arXiv:2103.00752.
- Perrier, E. (2021). Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Research. arXiv preprint arXiv:2102.04234.