Computability, Complexity, Consistency and Controllability: A Four C's Framework for cross-disciplinary Ethical Algorithm Research
This work addresses the problem of motivating cross-disciplinary collaboration for ethical algorithm research, which is crucial for advancing feasible proposals in algorithmic governance, though it is incremental as it provides a framework rather than new empirical results.
The paper tackles the challenge of cross-disciplinary collaboration for ethical algorithm research by proposing a Four C's Framework covering computability, complexity, consistency, and controllability to assist researchers from diverse fields in addressing ethical design and regulation of algorithms, with the result being a structured approach to foster understanding and feasibility in ethical algorithmic governance.
The ethical consequences of, constraints upon and regulation of algorithms arguably represent the defining challenges of our age, asking us to reckon with the rise of computational technologies whose potential to radically transforming social and individual orders and identity in unforeseen ways is already being realised. Yet despite the multidisciplinary impact of this algorithmic turn, there remains some way to go in motivating the crossdisciplinary collaboration that is crucial to advancing feasible proposals for the ethical design, implementation and regulation of algorithmic and automated systems. In this work, we provide a framework to assist cross-disciplinary collaboration by presenting a Four C's Framework covering key computational considerations researchers across such diverse fields should consider when approaching these questions: (i) computability, (ii) complexity, (iii) consistency and (iv) controllability. In addition, we provide examples of how insights from ethics, philosophy and population ethics are relevant to and translatable within sciences concerned with the study and design of algorithms. Our aim is to set out a framework which we believe is useful for fostering cross-disciplinary understanding of pertinent issues in ethical algorithmic literature which is relevant considering the feasibility of ethical algorithmic governance, especially the impact of computational constraints upon algorithmic governance.