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Academy of Social Sciences - Five Ethical Principles for Social Science Research

Nov 03, 2021

Summary

These principles were drafted in response to the practice of applying ethical principles intended for the biomedical sciences into other domains. As Dingwell, et.al. (2017) write, "While these codes are often treated as if they were universally applicable, social scientists have demonstrated that they have been shaped by the particular circumstances and contingencies of biomedical research on human subjects."
The statement of principles is itself very short, capturing the essence of biomedical research ethics without including those aspects specific to that particular science. The principles endorse democratic values such as inclusion of different interests, values, funders, methods and perspectives. They also endorse respect for the privacy, autonomy, diversity, values, and dignity of individuals, groups and communities. They advocate for scientific integrity, social responsibilities, as well as maximizing benefit and minimizing harm.
Data Ethics
Before examining specific proposals regarding the ethics of data analytics and data research, it is worth considering ethics related to data in general. According to Floridi and Taddeo (2016), "data ethics can be defined as the branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values)." They are derived from information ethics, and reinforce the idea that "Social acceptability or, even better, social preferability must be the guiding principles for any data science project with even a remote impact on human life, to ensure that opportunities will not be missed" (Ibid).
The following sets of standards, codes or principles have been developed regarding data ethics generally.

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