Above we focused on aspects of research ethics relevant to the research activity. However, research ethics expanded to focus on topics beyond human subject protection and social responsibilities to cover integrity, collegiality and even ways of working (e.g. , ; Pimple, 2002). For example, it goes without saying that scientific malpractice like data manipulation, falsification, fabrication and plagiarism are unethical. Computational social science does not make them ethical.
For computational social sciences, a clear additional dimension of ethics relates to access to digital data and skills. Almost 10 years ago, Boyd and Crawford (2012) asked that what kinds of scholarly divisions emerged because of differences in data access. Different scholars may have different levels of institutional credits to get access to large data sets, thus allowing only them to analyse the data - and in the end, making them more famous researchers, creating a self-enforcing loop. They and later (Bruns, 2019) highlight how control over data access may also limit what kinds of questions are examined through the data. However, limiting data access may be required to ensure privacy and trust to emerge between corporations who have the data and those who want to study the data. As in other ethical questions, we see a challenging balance between different perspectives. The debate about data access will most likely continue another 10 years, giving more accounts and insights into them.
Another aspect that Boyd and Crawford (2012) highlights is the skill dimension. There are those who can apply computational techniques and answer research questions - and then there are those who cannot. This book provides some answers to this question, which we will further address in Chapter 11.