Ethics and Human Rights Review of Codes of Ethics in computational and information sciences

Over 2017-19, I led a body of research at CIS which attempted to articulate an ethical framework to study big data practices. This is a four-part review of guideline documents for ethics and human rights in big data for development research. This research was produced as part of the Big Data for Development network supported by International Development Research Centre, Canada.

In the first part, we trace the history of ethical principles in biomedical research. While embarking on an exercise of evolving ethical guiding principles in big data for development and their application, we feel it important to begin with a review of the body of literature of ethical principles in other domains.

In the second part, we look at the role of ethical frameworks in the discipline of computer science. Due to the assumption that this discipline does not have clear impacts on human subjects of research, it has generally avoided establishing institutional ethical frameworks. However, in a few decades old history of these disciplines has been peppered with attempts to articulate ethical frameworks that could govern their practice and research.

In the third part, we analyse sixteen different codes of ethics which have evolved in computational and information sciences. While this list is not exhaustive, we have attempted to represent different kinds of stakeholders engaging in such exercises: Private sector, educational and academic institutions, global bodies representing professionals, civil society and states. Similarly, we have tried to represent attempts
from different related disciplines such as Data Science, Computing, Electronics & Electrical Engineering, Knowledge Theory, Statistics, Operations, Systems Administration, Information Theory and Artificial Intelligence.

Each of these guidelines were analysed for their adherence to well recognised principles from the fields of normative ethics, applied ethics and law. Based on this exercise, we have both high level conclusions about representations of principles across guidelines, as well as analysis of reasons for preferences made. While there have been several attempts at arriving at guidelines to govern the use of data and
algorithms, and some critical scholarship analysing them, the attempt behind this exercise is to provide a frame to recognise and evaluate them.

The fourth part documents an extended review of the sixteen codes of ethics against normative principles of utilitarianism, consequentialism, Kantianism and deontology; and virtue and applied ethics principles of social benefit, legality, awareness of misuse, benevolence and harm.

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